Detecting neoplasm

ABSTRACT

Provided herein is technology relating to detecting neoplasia and particularly, but not exclusively, to methods, compositions, and related uses for detecting premalignant and malignant neoplasms such as pancreatic and colorectal cancer.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a continuation of U.S. patent application Ser. No. 15/978,565, filed May 14, 2018 which is a continuation of U.S. patent application Ser. No. 14/775,435, filed Sep. 11, 2015, allowed as U.S. Pat. No. 9,994,911, which is a Section 371 U.S. National Stage entry of International Patent Application No. PCT/US2014/024589, international filing date, Mar. 12, 2014, which claims priority to expired U.S. Provisional Patent Application No. 61/784,429, filed Mar. 14, 2013, the contents of which are incorporated by reference in their entireties.

FIELD OF INVENTION

Provided herein is technology relating to detecting neoplasia and particularly, but not exclusively, to methods, compositions, and related uses for detecting premalignant and malignant neoplasms such as pancreatic and colorectal cancer.

BACKGROUND

In aggregate, gastrointestinal cancers account for more cancer mortality than any other organ system. While colorectal cancers are currently screened, annual US mortality from upper gastrointestinal cancers exceeds 90,000 compared to roughly 50,000 for colorectal cancer. Strikingly, 43,000 men and women are diagnosed each year with pancreatic cancer (PanC), which will cause nearly 37,000 deaths annually (Jemal et al. (2010) “Cancer statistics” CA Cancer J Clin 60: 277-300). As a result, PanC is the fourth leading cause of cancer deaths (id). Patients who present with symptoms typically already have advanced stage disease and only 15% meet criteria for potentially curative surgery (Ghaneh et al. (2007) “Biology and management of pancreatic cancer” Gut 56: 1134-52). Despite surgery, 85% will die of recurrent disease (Sohn et al. (2000) “Resected adenocarcinoma of the pancreas-616 patients: results, outcomes, and prognostic indicators” J Gastrointest Surg 4: 567-79). PanC mortality exceeds 95% and the 5-year survival rate is less than 25% for patients having curative surgery (Cleary et al (2004) “Prognostic factors in resected pancreatic adenocarcinoma: analysis of actual 5-year survivors” J Am Coll Surg 198: 722-31: Yeo et al (1995) “Pancreaticoduodenectomy for cancer of the head of the pancreas. 201 patients” Ann Surg 221: 721-33).

Among patients with syndromic predisposition to PanC or strong family history, aggressive, invasive screening strategies using computed tomography scans or endoscopic ultrasound have shown a 10% yield for neoplasia (Canto et al. (2006) “Screening for early pancreatic neoplasia in high-risk individuals: a prospective controlled study” Clin Gastroenterol Hepatol 4: 766-81). This screening strategy is impractical for the general population where most PanC arises without a known pre-disposition (Klein et al. (2001) “Familial pancreatic cancer” Cancer J 7: 266-73).

The nearly uniform lethality of PanC has generated intense interest in understanding pancreatic tumor biology. Precursor lesions have been identified, including pancreatic intraepithelial neoplasm (PanIN, grades I-III) and intraductal papillary mucinous neoplasm (IPMN) (Fernández-del Castillo et al. (2010) “Intraductal papillary mucinous neoplasms of the pancreas” Gastroenterology 139: 708-13, 713.e1-2; Haugk (2010) “Pancreatic intraepithelial neoplasia—can we detect early pancreatic cancer?” Histopathology 57: 503-14). Study of both precursors and malignant lesions has identified a number of molecular characteristics at genetic, epigenetic, and proteomic levels that could be exploited for therapy or used as biomarkers for early detection and screening (Kaiser (2008) “Cancer genetics. A detailed genetic portrait of the deadliest human cancers” Science 321: 1280-1; Omura et al. (2009) “Epigenetics and epigenetic alterations in pancreatic cancer” Int J Clin Exp Pathol 2: 310-26; Tonack et al. (2009) “Pancreatic cancer: proteomic approaches to a challenging disease” Pancreatology 9: 567-76). Recent tumor and metastatic lesion mapping studies have shown that there may be a significant latency period between the development of malignant PanC and the development of metastatic disease, suggesting a wide window of opportunity for detection and curative treatment of presymptomatic earliest-stage lesions (Yachida et al. (2010) “Distant metastasis occurs late during the genetic evolution of pancreatic cancer” Nature 467: 1114-7).

PanC sheds (e.g., exfoliates) cells and DNA into local effluent and ultimately into stool. For example, DNA containing a mutant KRAS gene can be identified (e.g., sequenced) from pancreatic juice of patients with pancreatic cancer, PanIN, and IPMN (Yamaguchi et al. (2005) “Pancreatic juice cytology in IPMN of the pancreas” Pancreatology 5: 416-21). Previously, highly sensitive assays have been used to detect mutant DNA in matched stools of pancreas cancer patients whose excised tumors were known to contain the same sequences (Zou et al (2009) “T2036 Pan-Detection of Gastrointestinal Neoplasms By Stool DNA Testing: Establishment of Feasibility” Gastroenterology 136: A-625). A limitation of mutation markers relates to the unwieldy process of their detection in conventional assays; typically, each mutational site across multiple genes must be assayed separately to achieve high sensitivity.

Methylated DNA has been studied as a potential class of biomarkers in the tissues of most tumor types. In many instances, DNA methyltransferases add a methyl group to DNA at cytosine-phosphate-guanine (CpG) island sites as an epigenetic control of gene expression. In a biologically attractive mechanism, acquired methylation events in promoter regions of tumor suppressor genes are thought to silence expression, thus contributing to oncogenesis. DNA methylation may be a more chemically and biologically stable diagnostic tool than RNA or protein expression (Laird (2010) “Principles and challenges of genome-wide DNA methylation analysis” Nat Rev Genet 11: 191-203). Furthermore, in other cancers like sporadic colon cancer, methylation markers offer excellent specificity and are more broadly informative and sensitive than are individual DNA mutations (Zou et al (2007) “Highly methylated genes in colorectal neoplasia: implications for screening” Cancer Epidemiol Biomarkers Prev 16: 2686-96).

Analysis of CpG islands has yielded important findings when applied to animal models and human cell lines. For example, Zhang and colleagues found that amplicons from different parts of the same CpG island may have different levels of methylation (Zhang et al. (2009) “DNA methylation analysis of chromosome 21 gene promoters at single base pair and single allele resolution” PLoS Genet 5: e1000438). Further, methylation levels were distributed bi-modally between highly methylated and unmethylated sequences, further supporting the binary switch-like pattern of DNA methyltransferase activity (Zhang et al. (2009) “DNA methylation analysis of chromosome 21 gene promoters at single base pair and single allele resolution” PLoS Genet 5: e1000438). Analysis of murine tissues in vivo and cell lines in vitro demonstrated that only about 0.3% of high CpG density promoters (HCP, defined as having >7% CpG sequence within a 300 base pair region) were methylated, whereas areas of low CpG density (LCP, defined as having <5% CpG sequence within a 300 base pair region) tended to be frequently methylated in a dynamic tissue-specific pattern (Meissner et al. (2008) “Genome-scale DNA methylation maps of pluripotent and differentiated cells” Nature 454: 766-70). HCPs include promoters for ubiquitous housekeeping genes and highly regulated developmental genes. Among the HCP sites methylated at >50% were several established markers such as Wnt 2, NDRG2, SFRP2, and BMP3 (Meissner et al. (2008) “Genome-scale DNA methylation maps of pluripotent and differentiated cells” Nature 454: 766-70).

For pancreatic cancer, PanIN, and IPMN lesions, marker methylation has been studied at the tissue level (Omura et al. (2008) “Genome-wide profiling of methylated promoters in pancreatic adenocarcinoma” Cancer Biol Ther 7: 1146-56; Sato et al. (2008) “CpG island methylation profile of pancreatic intraepithelial neoplasia” Mod Pathol 21: 238-44; Hong et al. (2008) “Multiple genes are hypermethylated in intraductal papillary mucinous neoplasms of the pancreas” Mod Pathol 21: 1499-507). For example, the markers MDFI, ZNF415, CNTNAP2, and ELOVL4 were highly methylated in 96%, 86%, 82%, and 68% of the cancers studied while, comparatively, only 9%, 6%, 3%, and 7% of control (non-cancerous) pancreata, respectively, were highly methylated at these same four loci (Omura et al. (2008) “Genome-wide profiling of methylated promoters in pancreatic adenocarcinoma” Cancer Biol Ther 7: 1146-56). It was found that measuring the methylation state of both MDFI and CNTNAP2 provided an indicator for pancreatic cancer that had both a high sensitivity (>90%) and a high specificity (>85%) (Omura et al. (2008) “Genome-wide profiling of methylated promoters in pancreatic adenocarcinoma” Cancer Biol Ther 7: 1146-56).

Furthermore, Sato and colleagues found eight genes differentially expressed in pancreatic cancer cell lines before and after treatment with a methyltransferase inhibitor (Sato et al. (2003) “Discovery of novel targets for aberrant methylation in pancreatic carcinoma using high-throughput microarrays” Cancer Res 63: 3735-42). These markers were subsequently assessed by methylation-specific PCR (MSP) of DNA from Pan-IN lesions. The results showed that SARP-2 (secreted frizzled related protein 1, SFRP1) had 83% sensitivity and could discriminate between Pan-IN 2 and higher grade Pan-IN 3 (Sato et al. (2008) “CpG island methylation profile of pancreatic intraepithelial neoplasia” Mod Pathol 21: 238-44). Discrimination of a high grade precursor or early stage cancer from a lower grade lesion is important when considering the morbidity of pancreaticoduodenectomy or distal pancreatectomy, the main surgical therapies for PanC. When studying both main-duct and side-branch IPMN precursors, Hong and colleagues showed high sensitivity and specificity for SFRP1 as well, especially in combination with UCHL1 (Hong et al. (2008) “Multiple genes are hypermethylated in intraductal papillary mucinous neoplasms of the pancreas” Mod Pathol 21: 1499-507). Tissue factor pathway inhibitor 2 (TFPI2) has a well-established tumor suppressor role in GU and GI malignancies, including prostate, cervical, colorectal, gastric, esophageal, and pancreatic cancers (Ma et al. (2011) “MicroRNA-616 induces androgen-independent growth of prostate cancer cells by suppressing expression of tissue factor pathway inhibitor TFPI-2” Cancer Res 71: 583-92; Lim et al. (2010) “Cervical dysplasia: assessing methylation status (Methylight) of CCNA1, DAPK1, HS3ST2, PAX1 and TFPI2 to improve diagnostic accuracy” Gynecol Oncol 119: 225-31; Hibi et al. (2010) “Methylation of TFPI2 gene is frequently detected in advanced well-differentiated colorectal cancer” Anticancer Res 30: 1205-7; Glockner et al. (2009) “Methylation of TFPI2 in stool DNA: a potential novel biomarker for the detection of colorectal cancer” Cancer Res 69: 4691-9; Hibi et al. (2010) “Methylation of the TFPI2 gene is frequently detected in advanced gastric carcinoma” Anticancer Res 30: 4131-3; Tsunoda et al. (2009) “Methylation of CLDN6, FBN2, RBP1, RBP4, TFPI2, and TMEFF2 in esophageal squamous cell carcinoma” Oncol Rep 21: 1067-73; Tang et al. (2010) “Prognostic significance of tissue factor pathway inhibitor-2 in pancreatic carcinoma and its effect on tumor invasion and metastasis” Med Oncol 27: 867-75; Brune et al. (2008) “Genetic and epigenetic alterations of familial pancreatic cancers” Cancer Epidemiol Biomarkers Prev 17: 3536-4). This marker has also been shown to be shed into the GI lumen and was 73% sensitive when assayed from pancreatic juice of cancers and normal subjects (Matsubayashi et al. (2006) “DNA methylation alterations in the pancreatic juice of patients with suspected pancreatic disease” Cancer Res 66: 1208-17).

TFPI2 was among a large number of potential mutation and methylation markers studied in tissue and stool samples as candidates for colorectal neoplasia. In a training-test set analysis of archival stools from almost 700 subjects, a multi-marker methylation panel, including TFPI2, BMP3, NDRG4, and vimentin was shown to have 85% sensitivity in CRC and 64% sensitivity in advanced colorectal adenomas, both at 90% specificity (Ahlquist D et al. (2010) “Next Generation Stool DNA Testing for Detection of Colorectal Neoplasia—Early Marker Evaluation”, presented at Colorectal Cancer: Biology to Therapy, American Association for Cancer Research).

Previous research has tested the performance of colorectal cancer methylation markers in PanC detection. In particular, a case-control study compared DNA from PanC tumor cases to DNA from colonic epithelia using MSP targeting markers previously reported in PanC (e.g., MDFI, SFRP2, UCHL1, CNTNAP2, and TFPI2) and additional discriminant colonic neoplasm markers (e.g., BMP3, EYA4, Vimentin, and NDRG4). A multi-marker regression model showed that EYA4, UCHL1, and MDFI were highly discriminant, with an area under the receiver operating characteristics curve of 0.85. As an individual marker, BMP3 was found to have an area under the receiver operator characteristics curve of 0.90. These four markers and mutant KRAS were subsequently assayed in a larger set of stool samples from PanC subjects in a blinded comparison to matched stools from individuals with a normal colonoscopy. Individually, BMP3 and KRAS were highly specific but poorly sensitive; in combination, sensitivity improved to 65% while maintaining 88% specificity (Kisiel, et al. (2011) “Stool DNA screening for colorectal cancer: opportunities to improve value with next generation tests” J Clin Gastroenterol 45: 301-8. These results suggested that methylation differences in UCHL1, EYA4, and MDFI at the level of the pancreas were obscured by background colonic methylation in the stool-based comparison. As such, cancer screening is in need of a marker or marker panel for PanC that is broadly informative and exhibits high specificity for PanC at the tissue level when interrogated in samples taken from a subject (e.g., a stool sample).

SUMMARY

Accordingly, provided herein is technology for pancreatic cancer screening markers and other gastrointestinal cancer screening markers that provide a high signal-to-noise ratio and a low background level when detected from samples taken from a subject (e.g., stool sample). Markers were identified in a case-control study by comparing the methylation state of DNA markers from tumors of subjects with stage I and stage II PanC to the methylation state of the same DNA markers from control subjects (e.g., normal tissue such as normal colon and/or non-neoplastic pancreas) (see, Examples 1 and 11).

Markers and/or panels of markers (e.g., a chromosomal region having an annotation selected from ABCB1, ADCY1, BHLHE23 (LOC63930), c13orf18, CACNA1C, chr12 133, CLEC11A, ELMO1, EOMES, CLEC 11, SHH, GJC1, IHIF1, IKZF1, KCNK12, KCNN2, PCBP3, PRKCB, RSPO3, SCARF2, SLC38A3, ST8SIA1, TWIST1, VWC2, WT1, and ZNF71) were identified in a case-control study by comparing the methylation state of DNA markers (e.g., from tumors of subjects with stage I and stage II PanC to the methylation state of the same DNA markers from control subjects (e.g., normal tissue such as normal colon and/or non-neoplastic pancreas) (see, Examples 2 and 8).

Markers and/or panels of markers (e.g., a chromosomal region having an annotation selected from NDRG4, SFRP1, BMP3, HPP1, and/or APC) were identified in case-control studies by comparing the methylation state of DNA markers from esophageal tissue of subjects with Barrett's esophagus to the methylation state of the same DNA markers from control subjects (see, Examples 4 and 10).

Markers and/or panels of markers (e.g., a chromosomal region having an annotation selected from ADCY1, PRKCB, KCNK12, C13ORF18, IKZF1, TWIST1, ELMO, 55957, CD1D, CLEC11A, KCNN2, BMP3, and/or NDRG4) were identified in case-control studies by comparing the methylation state of DNA markers from a pancreatic juice sample from subjects with pancreas cancer to the methylation state of the same DNA markers from control subjects (see, Examples 5 and 6).

A marker (e.g., a chromosomal region having a CD1D annotation) was identified in a case-control study by comparing the methylation state of a DNA marker (e.g., CD1D) from a stool sample from subjects with pancreas cancer to the methylation state of the same DNA marker from control subjects not having pancreas cancer (see, Example 7).

A marker (e.g., miR-1290) was identified in a case-control study by comparing the quantitated amount of a DNA marker (e.g., miR-1290) from a stool sample from subjects with pancreas cancer to the quantitated amount of the same DNA marker from control subjects not having pancreas cancer (see, Example 9).

Additional statistical analysis of the results demonstrated that the technology described herein based on these markers specifically and sensitively predicts a tumor site.

As described herein, the technology provides a number of methylated DNA markers and subsets thereof (e.g., sets of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more markers) with high discrimination for GI neoplasms overall and/or at individual tumor sites. Experiments applied a selection filter to candidate markers to identify markers that provide a high signal to noise ratio and a low background level to provide high specificity, e.g., when assaying distant media (e.g., stool, blood, urine, metastatic tissue, etc.) for purposes of cancer screening or diagnosis. Further, experiments were performed to demonstrate that the identified methylated DNA markers predict tumor site. As such, the technology provides for specific markers, marker combinations, and algorithms to predict tumor site.

In some embodiments, the technology is related to assessing the presence of and methylation state of one or more of the markers identified herein in a biological sample. These markers comprise one or more differentially methylated regions (DMR) as discussed herein, e.g., as provided in Table 1 and/or Table 10. Methylation state is assessed in embodiments of the technology. As such, the technology provided herein is not restricted in the method by which a gene's methylation state is measured. For example, in some embodiments the methylation state is measured by a genome scanning method. For example, one method involves restriction landmark genomic scanning (Kawai et al. (1994) Mol. Cell. Biol. 14: 7421-7427) and another example involves methylation-sensitive arbitrarily primed PCR (Gonzalgo et al. (1997) Cancer Res. 57: 594-599). In some embodiments, changes in methylation patterns at specific CpG sites are monitored by digestion of genomic DNA with methylation-sensitive restriction enzymes followed by Southern analysis of the regions of interest (digestion-Southern method). In some embodiments, analyzing changes in methylation patterns involves a PCR-based process that involves digestion of genomic DNA with methylation-sensitive restriction enzymes prior to PCR amplification (Singer-Sam et al. (1990) Nucl. Acids Res. 18: 687). In addition, other techniques have been reported that utilize bisulfite treatment of DNA as a starting point for methylation analysis. These include methylation-specific PCR (MSP) (Herman et al. (1992) Proc. Natl. Acad. Sci. USA 93: 9821-9826) and restriction enzyme digestion of PCR products amplified from bisulfite-converted DNA (Sadri and Hornsby (1996) Nucl. Acids Res. 24: 5058-5059; and Xiong and Laird (1997) Nucl. Acids Res. 25: 2532-2534). PCR techniques have been developed for detection of gene mutations (Kuppuswamy et al. (1991) Proc. Natl. Acad. Sci. USA 88: 1143-1147) and quantification of allelic-specific expression (Szabo and Mann (1995) Genes Dev. 9: 3097-3108; and Singer-Sam et al. (1992) PCR Methods Appl. 1: 160-163). Such techniques use internal primers, which anneal to a PCR-generated template and terminate immediately 5′ of the single nucleotide to be assayed. Methods using a “quantitative Ms-SNuPE assay” as described in U.S. Pat. No. 7,037,650 are used in some embodiments.

Upon evaluating a methylation state, the methylation state is often expressed as the fraction or percentage of individual strands of DNA that is methylated at a particular site (e.g., at a single nucleotide, at a particular region or locus, at a longer sequence of interest, e.g., up to a ˜100-bp, 200-bp, 500-bp, 1000-bp subsequence of a DNA or longer) relative to the total population of DNA in the sample comprising that particular site. Traditionally, the amount of the unmethylated nucleic acid is determined by PCR using calibrators. Then, a known amount of DNA is bisulfite treated and the resulting methylation-specific sequence is determined using either a real-time PCR or other exponential amplification, e.g., a QuARTS assay (e.g., as provided by U.S. Pat. No. 8,361,720; and U.S. Pat. Appl. Pub. Nos. 2012/0122088 and 2012/0122106, incorporated herein by reference).

For example, in some embodiments methods comprise generating a standard curve for the unmethylated target by using external standards. The standard curve is constructed from at least two points and relates the real-time Ct value for unmethylated DNA to known quantitative standards. Then, a second standard curve for the methylated target is constructed from at least two points and external standards. This second standard curve relates the Ct for methylated DNA to known quantitative standards. Next, the test sample Ct values are determined for the methylated and unmethylated populations and the genomic equivalents of DNA are calculated from the standard curves produced by the first two steps. The percentage of methylation at the site of interest is calculated from the amount of methylated DNAs relative to the total amount of DNAs in the population, e.g., (number of methylated DNAs)/(the number of methylated DNAs+ number of unmethylated DNAs)×100.

Also provided herein are compositions and kits for practicing the methods. For example, in some embodiments, reagents (e.g., primers, probes) specific for one or more markers are provided alone or in sets (e.g., sets of primers pairs for amplifying a plurality of markers). Additional reagents for conducting a detection assay may also be provided (e.g., enzymes, buffers, positive and negative controls for conducting QuARTS, PCR, sequencing, bisulfite, or other assays). In some embodiments, the kits containing one or more reagent necessary, sufficient, or useful for conducting a method are provided. Also provided are reactions mixtures containing the reagents. Further provided are master mix reagent sets containing a plurality of reagents that may be added to each other and/or to a test sample to complete a reaction mixture.

In some embodiments, the technology described herein is associated with a programmable machine designed to perform a sequence of arithmetic or logical operations as provided by the methods described herein. For example, some embodiments of the technology are associated with (e.g., implemented in) computer software and/or computer hardware. In one aspect, the technology relates to a computer comprising a form of memory, an element for performing arithmetic and logical operations, and a processing element (e.g., a microprocessor) for executing a series of instructions (e.g., a method as provided herein) to read, manipulate, and store data. In some embodiments, a microprocessor is part of a system for determining a methylation state (e.g., of one or more DMR, e.g., DMR 1-107 as provided in Table 1, e.g., DMR 1-449 in Table 10); comparing methylation states (e.g., of one or more DMR, e.g., DMR 1-107 as provided in Table 1, e.g., DMR 1-449 in Table 10); generating standard curves; determining a Ct value; calculating a fraction, frequency, or percentage of methylation (e.g., of one or more DMR, e.g., DMR 1-107 as provided in Table 1, e.g., DMR 1-449 in Table 10); identifying a CpG island; determining a specificity and/or sensitivity of an assay or marker; calculating an ROC curve and an associated AUC: sequence analysis; all as described herein or is known in the art.

In some embodiments, a microprocessor or computer uses methylation state data in an algorithm to predict a site of a cancer.

In some embodiments, a software or hardware component receives the results of multiple assays and determines a single value result to report to a user that indicates a cancer risk based on the results of the multiple assays (e.g., determining the methylation state of multiple DMR, e.g., as provided in Table 1, e.g., as provided in Table 10). Related embodiments calculate a risk factor based on a mathematical combination (e.g., a weighted combination, a linear combination) of the results from multiple assays, e.g., determining the methylation states of multiple markers (such as multiple DMR, e.g., as provided in Table 1, e.g., as provided in Table 10). In some embodiments, the methylation state of a DMR defines a dimension and may have values in a multidimensional space and the coordinate defined by the methylation states of multiple DMR is a result, e.g., to report to a user, e.g., related to a cancer risk.

Some embodiments comprise a storage medium and memory components. Memory components (e.g., volatile and/or nonvolatile memory) find use in storing instructions (e.g., an embodiment of a process as provided herein) and/or data (e.g., a work piece such as methylation measurements, sequences, and statistical descriptions associated therewith). Some embodiments relate to systems also comprising one or more of a CPU, a graphics card, and a user interface (e.g., comprising an output device such as display and an input device such as a keyboard).

Programmable machines associated with the technology comprise conventional extant technologies and technologies in development or yet to be developed (e.g., a quantum computer, a chemical computer, a DNA computer, an optical computer, a spintronics based computer, etc.).

In some embodiments, the technology comprises a wired (e.g., metallic cable, fiber optic) or wireless transmission medium for transmitting data. For example, some embodiments relate to data transmission over a network (e.g., a local area network (LAN), a wide area network (WAN), an ad-hoc network, the internet, etc.). In some embodiments, programmable machines are present on such a network as peers and in some embodiments the programmable machines have a client/server relationship.

In some embodiments, data are stored on a computer-readable storage medium such as a hard disk, flash memory, optical media, a floppy disk, etc.

In some embodiments, the technology provided herein is associated with a plurality of programmable devices that operate in concert to perform a method as described herein. For example, in some embodiments, a plurality of computers (e.g., connected by a network) may work in parallel to collect and process data, e.g., in an implementation of cluster computing or grid computing or some other distributed computer architecture that relies on complete computers (with onboard CPUs, storage, power supplies, network interfaces, etc.) connected to a network (private, public, or the internet) by a conventional network interface, such as Ethernet, fiber optic, or by a wireless network technology.

For example, some embodiments provide a computer that includes a computer-readable medium. The embodiment includes a random access memory (RAM) coupled to a processor. The processor executes computer-executable program instructions stored in memory. Such processors may include a microprocessor, an ASIC, a state machine, or other processor, and can be any of a number of computer processors, such as processors from Intel Corporation of Santa Clara, Calif. and Motorola Corporation of Schaumburg, Ill. Such processors include, or may be in communication with, media, for example computer-readable media, which stores instructions that, when executed by the processor, cause the processor to perform the steps described herein.

Embodiments of computer-readable media include, but are not limited to, an electronic, optical, magnetic, or other storage or transmission device capable of providing a processor with computer-readable instructions. Other examples of suitable media include, but are not limited to, a floppy disk, CD-ROM, DVD, magnetic disk, memory chip, ROM, RAM, an ASIC, a configured processor, all optical media, all magnetic tape or other magnetic media, or any other medium from which a computer processor can read instructions. Also, various other forms of computer-readable media may transmit or carry instructions to a computer, including a router, private or public network, or other transmission device or channel, both wired and wireless. The instructions may comprise code from any suitable computer-programming language, including, for example, C, C++, C #, Visual Basic, Java, Python, Perl, and JavaScript.

Computers are connected in some embodiments to a network. Computers may also include a number of external or internal devices such as a mouse, a CD-ROM, DVD, a keyboard, a display, or other input or output devices. Examples of computers are personal computers, digital assistants, personal digital assistants, cellular phones, mobile phones, smart phones, pagers, digital tablets, laptop computers, internet appliances, and other processor-based devices. In general, the computers related to aspects of the technology provided herein may be any type of processor-based platform that operates on any operating system, such as Microsoft Windows, Linux, UNIX, Mac OS X, etc., capable of supporting one or more programs comprising the technology provided herein. Some embodiments comprise a personal computer executing other application programs (e.g., applications). The applications can be contained in memory and can include, for example, a word processing application, a spreadsheet application, an email application, an instant messenger application, a presentation application, an Internet browser application, a calendar/organizer application, and any other application capable of being executed by a client device.

All such components, computers, and systems described herein as associated with the technology may be logical or virtual.

Accordingly, provided herein is technology related to a method of screening for a neoplasm in a sample obtained from a subject, the method comprising assaying a methylation state of a marker in a sample obtained from a subject; and identifying the subject as having a neoplasm when the methylation state of the marker is different than a methylation state of the marker assayed in a subject that does not have a neoplasm, wherein the marker comprises a base in a differentially methylated region (DMR) selected from a group consisting of DMR 1-107 as provided in Table 1 and/or DMR 1-449 in Table 10. In some embodiments, the method further comprises locating the neoplasm site within the subject, wherein the methylation state of the marker indicates the neoplasm site within the subject. The technology is related to identifying and discriminating gastrointestinal cancers, e.g., in some embodiments the neoplasm is a gastrointestinal neoplasm. In some embodiments, the neoplasm is present in the upper gastrointestinal area of the patient and in some embodiments the neoplasm is present in the lower gastrointestinal area of the patient. In particular embodiments, the neoplasm is a pancreas neoplasm, a colorectal neoplasm, a bile duct neoplasm, or an adenoma. The technology also encompasses determining the state or stage of a cancer, e.g., in some embodiments the neoplasm is pre-cancerous. Some embodiments provide methods comprising assaying a plurality of markers, e.g., comprising assaying 2 to 11 markers.

The technology is not limited in the methylation state assessed. In some embodiments assessing the methylation state of the marker in the sample comprises determining the methylation state of one base. In some embodiments, assaying the methylation state of the marker in the sample comprises determining the extent of methylation at a plurality of bases. Moreover, in some embodiments the methylation state of the marker comprises an increased methylation of the marker relative to a normal methylation state of the marker. In some embodiments, the methylation state of the marker comprises a decreased methylation of the marker relative to a normal methylation state of the marker. In some embodiments the methylation state of the marker comprises a different pattern of methylation of the marker relative to a normal methylation state of the marker.

Furthermore, in some embodiments the marker is a region of 100 or fewer bases, the marker is a region of 500 or fewer bases, the marker is a region of 1000 or fewer bases, the marker is a region of 5000 or fewer bases, or, in some embodiments, the marker is one base. In some embodiments the marker is in a high CpG density promoter.

The technology is not limited by sample type. For example, in some embodiments the sample is a stool sample, a tissue sample, a pancreatic juice sample, a pancreatic cyst fluid sample, a blood sample (e.g., plasma, serum, whole blood), an excretion, or a urine sample.

Furthermore, the technology is not limited in the method used to determine methylation state. In some embodiments the assaying comprises using methylation specific polymerase chain reaction, nucleic acid sequencing, mass spectrometry, methylation specific nuclease, mass-based separation, or target capture. In some embodiments, the assaying comprises use of a methylation specific oligonucleotide. In some embodiments, the technology uses massively parallel sequencing (e.g., next-generation sequencing) to determine methylation state, e.g., sequencing-by-synthesis, real-time (e.g., single-molecule) sequencing, bead emulsion sequencing, nanopore sequencing, etc.

The technology provides reagents for detecting a DMR, e.g., in some embodiments are provided a set of oligonucleotides comprising the sequences provided by SEQ ID NO: 1-202. In some embodiments are provided an oligonucleotide comprising a sequence complementary to a chromosomal region having a base in a DMR, e.g., an oligonucleotide sensitive to methylation state of a DMR.

The technology provides various panels of markers, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is ABCB1, ADCY1, BHLHE23 (LOC63930), c13orf18, CACNA1C, chr12.133, CLEC11A, ELMO1, EOMES, GJC1, IHIF1, IKZF1, KCNK12, KCNN2, NDRG4, PCBP3, PRKCB, RSPO3, SCARF2, SLC38A3, ST8SIA1, TWIST1, VWC2, WT1, or ZNF71, and that comprises the marker (see, Tables 1 and 9). In addition, embodiments provide a method of analyzing a DMR from Table 1 that is DMR No. 11, 14, 15, 65, 21, 22, 23, 5, 29, 30, 38, 39, 41, 50, 51, 55, 57, 60, 61, 8, 75, 81, 82, 84, 87, 93, 94, 98, 99, 103, 104, or 107, and/or a DMR corresponding to Chr16:58497395-58497458. Some embodiments provide determining the methylation state of a marker, wherein a chromosomal region having an annotation that is CLEC11A, C13ORF18, KCNN2, ABCB1, SLC38A3, CD1D, IKZF1, ADCY1, CHR12133, RSPO3, MBP3, PRKCB, NDRG4, ELMO, or TWIST1 comprises the marker. In some embodiments, the methods comprise determining the methylation state of two markers, e.g., a pair of markers provided in a row of Table 5.

Kit embodiments are provided, e.g., a kit comprising a bisulfite reagent; and a control nucleic acid comprising a sequence from a DMR selected from a group consisting of DMR 1-107 (from Table 1) and/or a DMR selected from a group consisting of DMR 1-449 (from Table 10) and having a methylation state associated with a subject who does not have a cancer. In some embodiments, kits comprise a bisulfite reagent and an oligonucleotide as described herein. In some embodiments, kits comprise a bisulfite reagent; and a control nucleic acid comprising a sequence from a DMR selected from a group consisting of DMR 1-107 (from Table 1) and/or DMR 1-449 (from Table 10) and having a methylation state associated with a subject who has a cancer. Some kit embodiments comprise a sample collector for obtaining a sample from a subject (e.g., a stool sample); reagents for isolating a nucleic acid from the sample; a bisulfite reagent; and an oligonucleotide as described herein.

The technology is related to embodiments of compositions (e.g., reaction mixtures). In some embodiments are provided a composition comprising a nucleic acid comprising a DMR and a bisulfite reagent. Some embodiments provide a composition comprising a nucleic acid comprising a DMR and an oligonucleotide as described herein. Some embodiments provide a composition comprising a nucleic acid comprising a DMR and a methylation-sensitive restriction enzyme. Some embodiments provide a composition comprising a nucleic acid comprising a DMR and a polymerase.

Additional related method embodiments are provided for screening for a neoplasm in a sample obtained from a subject, e.g., a method comprising determining a methylation state of a marker in the sample comprising a base in a DMR that is one or more of DMR 1-107 (from Table 1) and/or one or more of DMR 1-449 (from Table 10); comparing the methylation state of the marker from the subject sample to a methylation state of the marker from a normal control sample from a subject who does not have a cancer; and determining a confidence interval and/or a p value of the difference in the methylation state of the subject sample and the normal control sample. In some embodiments, the confidence interval is 90%, 95%, 97.5%, 98%, 99%, 99.5%, 99.9% or 99.99% and the p value is 0.1, 0.05, 0.025, 0.02, 0.01, 0.005, 0.001, or 0.0001. Some embodiments of methods provide steps of reacting a nucleic acid comprising a DMR with a bisulfite reagent to produce a bisulfite-reacted nucleic acid; sequencing the bisulfite-reacted nucleic acid to provide a nucleotide sequence of the bisulfite-reacted nucleic acid; comparing the nucleotide sequence of the bisulfite-reacted nucleic acid with a nucleotide sequence of a nucleic acid comprising the DMR from a subject who does not have a cancer to identify differences in the two sequences; and identifying the subject as having a neoplasm when a difference is present.

Systems for screening for a neoplasm in a sample obtained from a subject are provided by the technology. Exemplary embodiments of systems include, e.g., a system for screening for a neoplasm in a sample obtained from a subject, the system comprising an analysis component configured to determine the methylation state of a sample, a software component configured to compare the methylation state of the sample with a control sample or a reference sample methylation state recorded in a database, and an alert component configured to alert a user of a cancer-associated methylation state. An alert is determined in some embodiments by a software component that receives the results from multiple assays (e.g., determining the methylation states of multiple markers, e.g., DMR, e.g., as provided in Table 1, e.g., as provided in Table 10) and calculating a value or result to report based on the multiple results. Some embodiments provide a database of weighted parameters associated with each DMR provided herein for use in calculating a value or result and/or an alert to report to a user (e.g., such as a physician, nurse, clinician, etc.). In some embodiments all results from multiple assays are reported and in some embodiments one or more results are used to provide a score, value, or result based on a composite of one or more results from multiple assays that is indicative of a cancer risk in a subject.

In some embodiments of systems, a sample comprises a nucleic acid comprising a DMR. In some embodiments the system further comprises a component for isolating a nucleic acid, a component for collecting a sample such as a component for collecting a stool sample. In some embodiments, the system comprises nucleic acid sequences comprising a DMR. In some embodiments the database comprises nucleic acid sequences from subjects who do not have a cancer. Also provided are nucleic acids, e.g., a set of nucleic acids, each nucleic acid having a sequence comprising a DMR. In some embodiments the set of nucleic acids wherein each nucleic acid has a sequence from a subject who does not have a cancer. Related system embodiments comprise a set of nucleic acids as described and a database of nucleic acid sequences associated with the set of nucleic acids. Some embodiments further comprise a bisulfite reagent. And, some embodiments further comprise a nucleic acid sequencer.

Additional embodiments will be apparent to persons skilled in the relevant art based on the teachings contained herein.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the present technology will become better understood with regard to the following drawings:

FIG. 1 is a plot showing the marker importance of a subset of methylation markers as measured by Mean Decrease in Accuracy for Site Prediction.

FIG. 2 shows marker levels of BMP3 and NDRG4 in brushings (cardia+whole esophagus) in Barrett's cases and controls as described in Example 8.

FIG. 3 shows AUC of stool miR-1290 as described in Example 9.

It is to be understood that the figures are not necessarily drawn to scale, nor are the objects in the figures necessarily drawn to scale in relationship to one another. The figures are depictions that are intended to bring clarity and understanding to various embodiments of apparatuses, systems, compositions, and methods disclosed herein. Wherever possible, the same reference numbers are used throughout the drawings to refer to the same or like parts. Moreover, it should be appreciated that the drawings are not intended to limit the scope of the present teachings in any way.

DETAILED DESCRIPTION

Provided herein is technology relating to detecting neoplasia and particularly, but not exclusively, to methods, compositions, and related uses for detecting premalignant and malignant neoplasms such as pancreatic and colorectal cancer. As the technology is described herein, the section headings used are for organizational purposes only and are not to be construed as limiting the subject matter in any way.

In this detailed description of the various embodiments, for purposes of explanation, numerous specific details are set forth to provide a thorough understanding of the embodiments disclosed. One skilled in the art will appreciate, however, that these various embodiments may be practiced with or without these specific details. In other instances, structures and devices are shown in block diagram form. Furthermore, one skilled in the art can readily appreciate that the specific sequences in which methods are presented and performed are illustrative and it is contemplated that the sequences can be varied and still remain within the spirit and scope of the various embodiments disclosed herein.

All literature and similar materials cited in this application, including but not limited to, patents, patent applications, articles, books, treatises, and internet web pages are expressly incorporated by reference in their entirety for any purpose. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as is commonly understood by one of ordinary skill in the art to which the various embodiments described herein belongs. When definitions of terms in incorporated references appear to differ from the definitions provided in the present teachings, the definition provided in the present teachings shall control.

Definitions

To facilitate an understanding of the present technology, a number of terms and phrases are defined below. Additional definitions are set forth throughout the detailed description.

Throughout the specification and claims, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise. The phrase “in one embodiment” as used herein does not necessarily refer to the same embodiment, though it may. Furthermore, the phrase “in another embodiment” as used herein does not necessarily refer to a different embodiment, although it may. Thus, as described below, various embodiments of the invention may be readily combined, without departing from the scope or spirit of the invention.

In addition, as used herein, the term “or” is an inclusive “or” operator and is equivalent to the term “and/or” unless the context clearly dictates otherwise. The term “based on” is not exclusive and allows for being based on additional factors not described, unless the context clearly dictates otherwise. In addition, throughout the specification, the meaning of “a”, “an”, and “the” include plural references. The meaning of “in” includes “in” and “on.”

As used herein, a “nucleic acid” or “nucleic acid molecule” generally refers to any ribonucleic acid or deoxyribonucleic acid, which may be unmodified or modified DNA or RNA. “Nucleic acids” include, without limitation, single- and double-stranded nucleic acids. As used herein, the term “nucleic acid” also includes DNA as described above that contains one or more modified bases. Thus, DNA with a backbone modified for stability or for other reasons is a “nucleic acid”. The term “nucleic acid” as it is used herein embraces such chemically, enzymatically, or metabolically modified forms of nucleic acids, as well as the chemical forms of DNA characteristic of viruses and cells, including for example, simple and complex cells.

The terms “oligonucleotide” or “polynucleotide” or “nucleotide” or “nucleic acid” refer to a molecule having two or more deoxyribonucleotides or ribonucleotides, preferably more than three, and usually more than ten. The exact size will depend on many factors, which in turn depends on the ultimate function or use of the oligonucleotide. The oligonucleotide may be generated in any manner, including chemical synthesis, DNA replication, reverse transcription, or a combination thereof. Typical deoxyribonucleotides for DNA are thymine, adenine, cytosine, and guanine. Typical ribonucleotides for RNA are uracil, adenine, cytosine, and guanine.

As used herein, the terms “locus” or “region” of a nucleic acid refer to a subregion of a nucleic acid, e.g., a gene on a chromosome, a single nucleotide, a CpG island, etc.

The terms “complementary” and “complementarity” refer to nucleotides (e.g., 1 nucleotide) or polynucleotides (e.g., a sequence of nucleotides) related by the base-pairing rules. For example, the sequence 5′-A-G-T-3′ is complementary to the sequence 3′-T-C-A-5′. Complementarity may be “partial,” in which only some of the nucleic acids' bases are matched according to the base pairing rules. Or, there may be “complete” or “total” complementarity between the nucleic acids. The degree of complementarity between nucleic acid strands effects the efficiency and strength of hybridization between nucleic acid strands. This is of particular importance in amplification reactions and in detection methods that depend upon binding between nucleic acids.

The term “gene” refers to a nucleic acid (e.g., DNA or RNA) sequence that comprises coding sequences necessary for the production of an RNA, or of a polypeptide or its precursor. A functional polypeptide can be encoded by a full length coding sequence or by any portion of the coding sequence as long as the desired activity or functional properties (e.g., enzymatic activity, ligand binding, signal transduction, etc.) of the polypeptide are retained. The term “portion” when used in reference to a gene refers to fragments of that gene. The fragments may range in size from a few nucleotides to the entire gene sequence minus one nucleotide. Thus, “a nucleotide comprising at least a portion of a gene” may comprise fragments of the gene or the entire gene.

The term “gene” also encompasses the coding regions of a structural gene and includes sequences located adjacent to the coding region on both the 5′ and 3′ ends, e.g., for a distance of about 1 kb on either end, such that the gene corresponds to the length of the full-length mRNA (e.g., comprising coding, regulatory, structural and other sequences). The sequences that are located 5′ of the coding region and that are present on the mRNA are referred to as 5′ non-translated or untranslated sequences. The sequences that are located 3′ or downstream of the coding region and that are present on the mRNA are referred to as 3′ non-translated or 3′ untranslated sequences. The term “gene” encompasses both cDNA and genomic forms of a gene. In some organisms (e.g., eukaryotes), a genomic form or clone of a gene contains the coding region interrupted with non-coding sequences termed “introns” or “intervening regions” or “intervening sequences.” Introns are segments of a gene that are transcribed into nuclear RNA (hnRNA); introns may contain regulatory elements such as enhancers. Introns are removed or “spliced out” from the nuclear or primary transcript; introns therefore are absent in the messenger RNA (mRNA) transcript. The mRNA functions during translation to specify the sequence or order of amino acids in a nascent polypeptide.

In addition to containing introns, genomic forms of a gene may also include sequences located on both the 5′ and 3′ ends of the sequences that are present on the RNA transcript. These sequences are referred to as “flanking” sequences or regions (these flanking sequences are located 5′ or 3′ to the non-translated sequences present on the mRNA transcript). The 5′ flanking region may contain regulatory sequences such as promoters and enhancers that control or influence the transcription of the gene. The 3′ flanking region may contain sequences that direct the termination of transcription, posttranscriptional cleavage, and polyadenylation.

The term “wild-type” when made in reference to a gene refers to a gene that has the characteristics of a gene isolated from a naturally occurring source. The term “wild-type” when made in reference to a gene product refers to a gene product that has the characteristics of a gene product isolated from a naturally occurring source. The term “naturally-occurring” as applied to an object refers to the fact that an object can be found in nature. For example, a polypeptide or polynucleotide sequence that is present in an organism (including viruses) that can be isolated from a source in nature and which has not been intentionally modified by the hand of a person in the laboratory is naturally-occurring. A wild-type gene is often that gene or allele that is most frequently observed in a population and is thus arbitrarily designated the “normal” or “wild-type” form of the gene. In contrast, the term “modified” or “mutant” when made in reference to a gene or to a gene product refers, respectively, to a gene or to a gene product that displays modifications in sequence and/or functional properties (e.g., altered characteristics) when compared to the wild-type gene or gene product. It is noted that naturally-occurring mutants can be isolated; these are identified by the fact that they have altered characteristics when compared to the wild-type gene or gene product.

The term “allele” refers to a variation of a gene; the variations include but are not limited to variants and mutants, polymorphic loci, and single nucleotide polymorphic loci, frameshift, and splice mutations. An allele may occur naturally in a population or it might arise during the lifetime of any particular individual of the population.

Thus, the terms “variant” and “mutant” when used in reference to a nucleotide sequence refer to a nucleic acid sequence that differs by one or more nucleotides from another, usually related, nucleotide acid sequence. A “variation” is a difference between two different nucleotide sequences; typically, one sequence is a reference sequence.

“Amplification” is a special case of nucleic acid replication involving template specificity. It is to be contrasted with non-specific template replication (e.g., replication that is template-dependent but not dependent on a specific template). Template specificity is here distinguished from fidelity of replication (e.g., synthesis of the proper polynucleotide sequence) and nucleotide (ribo- or deoxyribo-) specificity. Template specificity is frequently described in terms of“target” specificity. Target sequences are “targets” in the sense that they are sought to be sorted out from other nucleic acid. Amplification techniques have been designed primarily for this sorting out.

Amplification of nucleic acids generally refers to the production of multiple copies of a polynucleotide, or a portion of the polynucleotide, typically starting from a small amount of the polynucleotide (e.g., a single polynucleotide molecule, 10 to 100 copies of a polynucleotide molecule, which may or may not be exactly the same), where the amplification products or amplicons are generally detectable. Amplification of polynucleotides encompasses a variety of chemical and enzymatic processes. The generation of multiple DNA copies from one or a few copies of a target or template DNA molecule during a polymerase chain reaction (PCR) or a ligase chain reaction (LCR; see, e.g., U.S. Pat. No. 5,494,810; herein incorporated by reference in its entirety) are forms of amplification. Additional types of amplification include, but are not limited to, allele-specific PCR (see, e.g., U.S. Pat. No. 5,639,611; herein incorporated by reference in its entirety), assembly PCR (see, e.g., U.S. Pat. No. 5,965,408; herein incorporated by reference in its entirety), helicase-dependent amplification (see, e.g., U.S. Pat. No. 7,662,594; herein incorporated by reference in its entirety), Hot-start PCR (see, e.g., U.S. Pat. Nos. 5,773,258 and 5,338,671; each herein incorporated by reference in their entireties), intersequence-specfic PCR, inverse PCR (see, e.g., Triglia, et al et al. (1988) Nucleic Acids Res., 16:8186; herein incorporated by reference in its entirety), ligation-mediated PCR (see, e.g., Guilfoyle, R. et al et al., Nucleic Acids Research, 25:1854-1858 (1997); U.S. Pat. No. 5,508,169; each of which are herein incorporated by reference in their entireties), methylation-specific PCR (see, e.g., Herman, et al., (1996) PNAS 93(13) 9821-9826; herein incorporated by reference in its entirety), miniprimer PCR, multiplex ligation-dependent probe amplification (see, e.g., Schouten, et al., (2002) Nucleic Acids Research 30(12): e57; herein incorporated by reference in its entirety), multiplex PCR (see, e.g., Chamberlain, et al., (1988) Nucleic Acids Research 16(23) 11141-11156; Ballabio, et al., (1990) Human Genetics 84(6) 571-573; Hayden, et al., (2008) BMC Genetics 9:80; each of which are herein incorporated by reference in their entireties), nested PCR, overlap-extension PCR (see, e.g., Higuchi, et al., (1988) Nucleic Acids Research 16(15) 7351-7367; herein incorporated by reference in its entirety), real time PCR (see, e.g., Higuchi, et al et al., (1992) Biotechnology 10:413-417; Higuchi, et al., (1993) Biotechnology 11:1026-1030; each of which are herein incorporated by reference in their entireties), reverse transcription PCR (see, e.g., Bustin, S. A. (2000) J. Molecular Endocrinology 25:169-193; herein incorporated by reference in its entirety), solid phase PCR, thermal asymmetric interlaced PCR, and Touchdown PCR (see, e.g., Don, et al., Nucleic Acids Research (1991) 19(14) 4008; Roux, K. (1994) Biotechniques 16(5) 812-814; Hecker, et al., (1996) Biotechniques 20(3) 478-485; each of which are herein incorporated by reference in their entireties). Polynucleotide amplification also can be accomplished using digital PCR (see, e.g., Kalinina, et al., Nucleic Acids Research. 25; 1999-2004, (1997); Vogelstein and Kinzler, Proc Natl Acad Sci USA. 96; 9236-41, (1999); International Patent Publication No. WO05023091A2; US Patent Application Publication No. 20070202525; each of which are incorporated herein by reference in their entireties).

The term “polymerase chain reaction” (“PCR”) refers to the method of KB. Mullis U.S. Pat. Nos. 4,683,195, 4,683,202, and 4,965,188, that describe a method for increasing the concentration of a segment of a target sequence in a mixture of genomic DNA without cloning or purification. This process for amplifying the target sequence consists of introducing a large excess of two oligonucleotide primers to the DNA mixture containing the desired target sequence, followed by a precise sequence of thermal cycling in the presence of a DNA polymerase. The two primers are complementary to their respective strands of the double stranded target sequence. To effect amplification, the mixture is denatured and the primers then annealed to their complementary sequences within the target molecule. Following annealing, the primers are extended with a polymerase so as to form a new pair of complementary strands. The steps of denaturation, primer annealing, and polymerase extension can be repeated many times (i.e., denaturation, annealing and extension constitute one “cycle”; there can be numerous “cycles”) to obtain a high concentration of an amplified segment of the desired target sequence. The length of the amplified segment of the desired target sequence is determined by the relative positions of the primers with respect to each other, and therefore, this length is a controllable parameter. By virtue of the repeating aspect of the process, the method is referred to as the “polymerase chain reaction” (“PCR”). Because the desired amplified segments of the target sequence become the predominant sequences (in terms of concentration) in the mixture, they are said to be “PCR amplified” and are “PCR products” or “amplicons.”

Template specificity is achieved in most amplification techniques by the choice of enzyme. Amplification enzymes are enzymes that, under conditions they are used, will process only specific sequences of nucleic acid in a heterogeneous mixture of nucleic acid. For example, in the case of Q-beta replicase, MDV-1 RNA is the specific template for the replicase (Kacian et al., Proc. Natl. Acad. Sci. USA, 69:3038 [1972]). Other nucleic acid will not be replicated by this amplification enzyme. Similarly, in the case of T7 RNA polymerase, this amplification enzyme has a stringent specificity for its own promoters (Chamberlin et al, Nature, 228:227 [1970]). In the case of T4 DNA ligase, the enzyme will not ligate the two oligonucleotides or polynucleotides, where there is a mismatch between the oligonucleotide or polynucleotide substrate and the template at the ligation junction (Wu and Wallace (1989) Genomics 4:560). Finally, thermostable template-dependant DNA polymerases (e.g., Taq and Pfu DNA polymerases), by virtue of their ability to function at high temperature, are found to display high specificity for the sequences bounded and thus defined by the primers; the high temperature results in thermodynamic conditions that favor primer hybridization with the target sequences and not hybridization with non-target sequences (H. A. Erlich (ed.), PCR Technology, Stockton Press [1989]).

As used herein, the term “nucleic acid detection assay” refers to any method of determining the nucleotide composition of a nucleic acid of interest. Nucleic acid detection assay include but are not limited to, DNA sequencing methods, probe hybridization methods, structure specific cleavage assays (e.g., the INVADER assay, Hologic, Inc.) and are described, e.g., in U.S. Pat. Nos. 5,846,717, 5,985,557, 5,994,069, 6,001,567, 6,090,543, and 6,872,816; Lyamichev et al., Nat. Biotech., 17:292 (1999), Hall et al., PNAS, USA, 97:8272 (2000), and US 2009/0253142, each of which is herein incorporated by reference in its entirety for all purposes); enzyme mismatch cleavage methods (e.g., Variagenics, U.S. Pat. Nos. 6,110,684, 5,958,692, 5,851,770, herein incorporated by reference in their entireties); polymerase chain reaction; branched hybridization methods (e.g., Chiron, U.S. Pat. Nos. 5,849,481, 5,710,264, 5,124,246, and 5,624,802, herein incorporated by reference in their entireties); rolling circle replication (e.g., U.S. Pat. Nos. 6,210,884, 6,183,960 and 6,235,502, herein incorporated by reference in their entireties); NASBA (e.g., U.S. Pat. No. 5,409,818, herein incorporated by reference in its entirety); molecular beacon technology (e.g., U.S. Pat. No. 6,150,097, herein incorporated by reference in its entirety); E-sensor technology (Motorola, U.S. Pat. Nos. 6,248,229, 6,221,583, 6,013,170, and 6,063,573, herein incorporated by reference in their entireties); cycling probe technology (e.g., U.S. Pat. Nos. 5,403,711, 5,011,769, and 5,660,988, herein incorporated by reference in their entireties); Dade Behring signal amplification methods (e.g., U.S. Pat. Nos. 6,121,001, 6,110,677, 5,914,230, 5,882,867, and 5,792,614, herein incorporated by reference in their entireties); ligase chain reaction (e.g., Barnay Proc. Natl. Acad. Sci USA 88, 189-93 (1991)); and sandwich hybridization methods (e.g., U.S. Pat. No. 5,288,609, herein incorporated by reference in its entirety).

The term “amplifiable nucleic acid” refers to a nucleic acid that may be amplified by any amplification method. It is contemplated that “amplifiable nucleic acid” will usually comprise “sample template.”

The term “sample template” refers to nucleic acid originating from a sample that is analyzed for the presence of “target” (defined below). In contrast, “background template” is used in reference to nucleic acid other than sample template that may or may not be present in a sample. Background template is most often inadvertent. It may be the result of carryover or it may be due to the presence of nucleic acid contaminants sought to be purified away from the sample. For example, nucleic acids from organisms other than those to be detected may be present as background in a test sample.

The term “primer” refers to an oligonucleotide, whether occurring naturally as in a purified restriction digest or produced synthetically, that is capable of acting as a point of initiation of synthesis when placed under conditions in which synthesis of a primer extension product that is complementary to a nucleic acid strand is induced, (e.g., in the presence of nucleotides and an inducing agent such as a DNA polymerase and at a suitable temperature and pH). The primer is preferably single stranded for maximum efficiency in amplification, but may alternatively be double stranded. If double stranded, the primer is first treated to separate its strands before being used to prepare extension products. Preferably, the primer is an oligodeoxyribonucleotide. The primer must be sufficiently long to prime the synthesis of extension products in the presence of the inducing agent. The exact lengths of the primers will depend on many factors, including temperature, source of primer, and the use of the method.

The term “probe” refers to an oligonucleotide (e.g., a sequence of nucleotides), whether occurring naturally as in a purified restriction digest or produced synthetically, recombinantly, or by PCR amplification, that is capable of hybridizing to another oligonucleotide of interest. A probe may be single-stranded or double-stranded. Probes are useful in the detection, identification, and isolation of particular gene sequences (e.g., a “capture probe”). It is contemplated that any probe used in the present invention may, in some embodiments, be labeled with any “reporter molecule,” so that is detectable in any detection system, including, but not limited to enzyme (e.g., ELISA, as well as enzyme-based histochemical assays), fluorescent, radioactive, and luminescent systems. It is not intended that the present invention be limited to any particular detection system or label.

As used herein, “methylation” refers to cytosine methylation at positions C5 or N4 of cytosine, the N6 position of adenine, or other types of nucleic acid methylation. In vitro amplified DNA is usually unmethylated because typical in vitro DNA amplification methods do not retain the methylation pattern of the amplification template. However, “unmethylated DNA” or “methylated DNA” can also refer to amplified DNA whose original template was unmethylated or methylated, respectively.

Accordingly, as used herein a “methylated nucleotide” or a “methylated nucleotide base” refers to the presence of a methyl moiety on a nucleotide base, where the methyl moiety is not present in a recognized typical nucleotide base. For example, cytosine does not contain a methyl moiety on its pyrimidine ring, but 5-methylcytosine contains a methyl moiety at position 5 of its pyrimidine ring. Therefore, cytosine is not a methylated nucleotide and 5-methylcytosine is a methylated nucleotide. In another example, thymine contains a methyl moiety at position 5 of its pyrimidine ring; however, for purposes herein, thymine is not considered a methylated nucleotide when present in DNA since thymine is a typical nucleotide base of DNA.

As used herein, a “methylated nucleic acid molecule” refers to a nucleic acid molecule that contains one or more methylated nucleotides.

As used herein, a “methylation state”, “methylation profile”, and “methylation status” of a nucleic acid molecule refers to the presence of absence of one or more methylated nucleotide bases in the nucleic acid molecule. For example, a nucleic acid molecule containing a methylated cytosine is considered methylated (e.g., the methylation state of the nucleic acid molecule is methylated). A nucleic acid molecule that does not contain any methylated nucleotides is considered unmethylated.

The methylation state of a particular nucleic acid sequence (e.g., a gene marker or DNA region as described herein) can indicate the methylation state of every base in the sequence or can indicate the methylation state of a subset of the bases (e.g., of one or more cytosines) within the sequence, or can indicate information regarding regional methylation density within the sequence with or without providing precise information of the locations within the sequence the methylation occurs.

The methylation state of a nucleotide locus in a nucleic acid molecule refers to the presence or absence of a methylated nucleotide at a particular locus in the nucleic acid molecule. For example, the methylation state of a cytosine at the 7th nucleotide in a nucleic acid molecule is methylated when the nucleotide present at the 7th nucleotide in the nucleic acid molecule is 5-methylcytosine. Similarly, the methylation state of a cytosine at the 7th nucleotide in a nucleic acid molecule is unmethylated when the nucleotide present at the 7th nucleotide in the nucleic acid molecule is cytosine (and not 5-methylcytosine).

The methylation status can optionally be represented or indicated by a “methylation value” (e.g., representing a methylation frequency, fraction, ratio, percent, etc.) A methylation value can be generated, for example, by quantifying the amount of intact nucleic acid present following restriction digestion with a methylation dependent restriction enzyme or by comparing amplification profiles after bisulfite reaction or by comparing sequences of bisulfite-treated and untreated nucleic acids. Accordingly, a value, e.g., a methylation value, represents the methylation status and can thus be used as a quantitative indicator of methylation status across multiple copies of a locus. This is of particular use when it is desirable to compare the methylation status of a sequence in a sample to a threshold or reference value.

As used herein, “methylation frequency” or “methylation percent (%)” refer to the number of instances in which a molecule or locus is methylated relative to the number of instances the molecule or locus is unmethylated.

As such, the methylation state describes the state of methylation of a nucleic acid (e.g., a genomic sequence). In addition, the methylation state refers to the characteristics of a nucleic acid segment at a particular genomic locus relevant to methylation. Such characteristics include, but are not limited to, whether any of the cytosine (C) residues within this DNA sequence are methylated, the location of methylated C residue(s), the frequency or percentage of methylated C throughout any particular region of a nucleic acid, and allelic differences in methylation due to, e.g., difference in the origin of the alleles. The terms “methylation state”, “methylation profile”, and “methylation status” also refer to the relative concentration, absolute concentration, or pattern of methylated C or unmethylated C throughout any particular region of a nucleic acid in a biological sample. For example, if the cytosine (C) residue(s) within a nucleic acid sequence are methylated it may be referred to as “hypermethylated” or having “increased methylation”, whereas if the cytosine (C) residue(s) within a DNA sequence are not methylated it may be referred to as “hypomethylated” or having “decreased methylation”. Likewise, if the cytosine (C) residue(s) within a nucleic acid sequence are methylated as compared to another nucleic acid sequence (e.g., from a different region or from a different individual, etc.) that sequence is considered hypermethylated or having increased methylation compared to the other nucleic acid sequence. Alternatively, if the cytosine (C) residue(s) within a DNA sequence are not methylated as compared to another nucleic acid sequence (e.g., from a different region or from a different individual, etc.) that sequence is considered hypomethylated or having decreased methylation compared to the other nucleic acid sequence. Additionally, the term “methylation pattern” as used herein refers to the collective sites of methylated and unmethylated nucleotides over a region of a nucleic acid. Two nucleic acids may have the same or similar methylation frequency or methylation percent but have different methylation patterns when the number of methylated and unmethylated nucleotides are the same or similar throughout the region but the locations of methylated and unmethylated nucleotides are different. Sequences are said to be “differentially methylated” or as having a “difference in methylation” or having a “different methylation state” when they differ in the extent (e.g., one has increased or decreased methylation relative to the other), frequency, or pattern of methylation. The term “differential methylation” refers to a difference in the level or pattern of nucleic acid methylation in a cancer positive sample as compared with the level or pattern of nucleic acid methylation in a cancer negative sample. It may also refer to the difference in levels or patterns between patients that have recurrence of cancer after surgery versus patients who not have recurrence. Differential methylation and specific levels or patterns of DNA methylation are prognostic and predictive biomarkers, e.g., once the correct cut-off or predictive characteristics have been defined.

Methylation state frequency can be used to describe a population of individuals or a sample from a single individual. For example, a nucleotide locus having a methylation state frequency of 50% is methylated in 50% of instances and unmethylated in 50% of instances. Such a frequency can be used, for example, to describe the degree to which a nucleotide locus or nucleic acid region is methylated in a population of individuals or a collection of nucleic acids. Thus, when methylation in a first population or pool of nucleic acid molecules is different from methylation in a second population or pool of nucleic acid molecules, the methylation state frequency of the first population or pool will be different from the methylation state frequency of the second population or pool. Such a frequency also can be used, for example, to describe the degree to which a nucleotide locus or nucleic acid region is methylated in a single individual. For example, such a frequency can be used to describe the degree to which a group of cells from a tissue sample are methylated or unmethylated at a nucleotide locus or nucleic acid region.

As used herein a “nucleotide locus” refers to the location of a nucleotide in a nucleic acid molecule. A nucleotide locus of a methylated nucleotide refers to the location of a methylated nucleotide in a nucleic acid molecule.

Typically, methylation of human DNA occurs on a dinucleotide sequence including an adjacent guanine and cytosine where the cytosine is located 5′ of the guanine (also termed CpG dinucleotide sequences). Most cytosines within the CpG dinucleotides are methylated in the human genome, however some remain unmethylated in specific CpG dinucleotide rich genomic regions, known as CpG islands (see, e.g., Antequera et al. (1990) Cell 62: 503-514).

As used herein, a “CpG island” refers to a G:C-rich region of genomic DNA containing an increased number of CpG dinucleotides relative to total genomic DNA. A CpG island can be at least 100, 200, or more base pairs in length, where the G:C content of the region is at least 50% and the ratio of observed CpG frequency over expected frequency is 0.6; in some instances, a CpG island can be at least 500 base pairs in length, where the G:C content of the region is at least 55%) and the ratio of observed CpG frequency over expected frequency is 0.65. The observed CpG frequency over expected frequency can be calculated according to the method provided in Gardiner-Garden et al (1987) J. Mol. Biol. 196: 261-281. For example, the observed CpG frequency over expected frequency can be calculated according to the formula R=(A×B)/(C×D), where R is the ratio of observed CpG frequency over expected frequency, A is the number of CpG dinucleotides in an analyzed sequence, B is the total number of nucleotides in the analyzed sequence, C is the total number of C nucleotides in the analyzed sequence, and D is the total number of G nucleotides in the analyzed sequence. Methylation state is typically determined in CpG islands, e.g., at promoter regions. It will be appreciated though that other sequences in the human genome are prone to DNA methylation such as CpA and CpT (see Ramsahoye (2000) Proc. Natl. Acad. Sci. USA 97: 5237-5242; Salmon and Kaye (1970) Biochim. Biophys. Acta. 204: 340-351; Grafstrom (1985) Nucleic Acids Res. 13: 2827-2842; Nyce (1986) Nucleic Acids Res. 14: 4353-4367; Woodcock (1987) Biochem. Biophys. Res. Commun. 145: 888-894).

As used herein, a reagent that modifies a nucleotide of the nucleic acid molecule as a function of the methylation state of the nucleic acid molecule, or a methylation-specific reagent, refers to a compound or composition or other agent that can change the nucleotide sequence of a nucleic acid molecule in a manner that reflects the methylation state of the nucleic acid molecule. Methods of treating a nucleic acid molecule with such a reagent can include contacting the nucleic acid molecule with the reagent, coupled with additional steps, if desired, to accomplish the desired change of nucleotide sequence. Such a change in the nucleic acid molecule's nucleotide sequence can result in a nucleic acid molecule in which each methylated nucleotide is modified to a different nucleotide. Such a change in the nucleic acid nucleotide sequence can result in a nucleic acid molecule in which each unmethylated nucleotide is modified to a different nucleotide. Such a change in the nucleic acid nucleotide sequence can result in a nucleic acid molecule in which each of a selected nucleotide which is unmethylated (e.g., each unmethylated cytosine) is modified to a different nucleotide. Use of such a reagent to change the nucleic acid nucleotide sequence can result in a nucleic acid molecule in which each nucleotide that is a methylated nucleotide (e.g., each methylated cytosine) is modified to a different nucleotide. As used herein, use of a reagent that modifies a selected nucleotide refers to a reagent that modifies one nucleotide of the four typically occurring nucleotides in a nucleic acid molecule (C, G, T, and A for DNA and C, G, U, and A for RNA), such that the reagent modifies the one nucleotide without modifying the other three nucleotides. In one exemplary embodiment, such a reagent modifies an unmethylated selected nucleotide to produce a different nucleotide. In another exemplary embodiment, such a reagent can deaminate unmethylated cytosine nucleotides. An exemplary reagent is bisulfite.

As used herein, the term “bisulfite reagent” refers to a reagent comprising in some embodiments bisulfite, disulfite, hydrogen sulfite, or combinations thereof to distinguish between methylated and unmethylated cytidines, e.g., in CpG dinucleotide sequences.

The term “methylation assay” refers to any assay for determining the methylation state of one or more CpG dinucleotide sequences within a sequence of a nucleic acid.

The term “MS AP-PCR” (Methylation-Sensitive Arbitrarily-Primed Polymerase Chain Reaction) refers to the art-recognized technology that allows for a global scan of the genome using CG-rich primers to focus on the regions most likely to contain CpG dinucleotides, and described by Gonzalgo et al. (1997) Cancer Research 57: 594-599.

The term “MethyLight™” refers to the art-recognized fluorescence-based real-time PCR technique described by Eads et al. (1999) Cancer Res. 59: 2302-2306.

The term “HeavyMethyl™” refers to an assay wherein methylation specific blocking probes (also referred to herein as blockers) covering CpG positions between, or covered by, the amplification primers enable methylation-specific selective amplification of a nucleic acid sample.

The term “HeavyMethyl™ MethyLight™” assay refers to a HeavyMethyl™ MethyLight™ assay, which is a variation of the MethyLight™ assay, wherein the MethyLight™ assay is combined with methylation specific blocking probes covering CpG positions between the amplification primers.

The term “Ms-SNuPE” (Methylation-sensitive Single Nucleotide Primer Extension) refers to the art-recognized assay described by Gonzalgo & Jones (1997) Nucleic Acids Res. 25: 2529-2531.

The term “MSP” (Methylation-specific PCR) refers to the art-recognized methylation assay described by Herman et al. (1996) Proc. Natl. Acad. Sci. USA 93: 9821-9826, and by U.S. Pat. No. 5,786,146.

The term “COBRA” (Combined Bisulfite Restriction Analysis) refers to the art-recognized methylation assay described by Xiong & Laird (1997) Nucleic Acids Res. 25: 2532-2534.

The term “MCA” (Methylated CpG Island Amplification) refers to the methylation assay described by Toyota et al. (1999) Cancer Res. 59: 2307-12, and in WO 00/26401A1.

As used herein, a “selected nucleotide” refers to one nucleotide of the four typically occurring nucleotides in a nucleic acid molecule (C, G, T, and A for DNA and C, G, U, and A for RNA), and can include methylated derivatives of the typically occurring nucleotides (e.g., when C is the selected nucleotide, both methylated and unmethylated C are included within the meaning of a selected nucleotide), whereas a methylated selected nucleotide refers specifically to a methylated typically occurring nucleotide and an unmethylated selected nucleotides refers specifically to an unmethylated typically occurring nucleotide.

The terms “methylation-specific restriction enzyme” or “methylation-sensitive restriction enzyme” refers to an enzyme that selectively digests a nucleic acid dependent on the methylation state of its recognition site. In the case of a restriction enzyme that specifically cuts if the recognition site is not methylated or is hemimethylated, the cut will not take place or will take place with a significantly reduced efficiency if the recognition site is methylated. In the case of a restriction enzyme that specifically cuts if the recognition site is methylated, the cut will not take place or will take place with a significantly reduced efficiency if the recognition site is not methylated. Preferred are methylation-specific restriction enzymes, the recognition sequence of which contains a CG dinucleotide (for instance a recognition sequence such as CGCG or CCCGGG). Further preferred for some embodiments are restriction enzymes that do not cut if the cytosine in this dinucleotide is methylated at the carbon atom C5.

As used herein, a “different nucleotide” refers to a nucleotide that is chemically different from a selected nucleotide, typically such that the different nucleotide has Watson-Crick base-pairing properties that differ from the selected nucleotide, whereby the typically occurring nucleotide that is complementary to the selected nucleotide is not the same as the typically occurring nucleotide that is complementary to the different nucleotide. For example, when C is the selected nucleotide, U or T can be the different nucleotide, which is exemplified by the complementarity of C to G and the complementarity of U or T to A. As used herein, a nucleotide that is complementary to the selected nucleotide or that is complementary to the different nucleotide refers to a nucleotide that base-pairs, under high stringency conditions, with the selected nucleotide or different nucleotide with higher affinity than the complementary nucleotide's base-paring with three of the four typically occurring nucleotides. An example of complementarity is Watson-Crick base pairing in DNA (e.g., A-T and C-G) and RNA (e.g., A-U and C-G). Thus, for example, G base-pairs, under high stringency conditions, with higher affinity to C than G base-pairs to G, A, or T and, therefore, when C is the selected nucleotide, G is a nucleotide complementary to the selected nucleotide.

As used herein, the “sensitivity” of a given marker refers to the percentage of samples that report a DNA methylation value above a threshold value that distinguishes between neoplastic and non-neoplastic samples. In some embodiments, a positive is defined as a histology-confirmed neoplasia that reports a DNA methylation value above a threshold value (e.g., the range associated with disease), and a false negative is defined as a histology-confirmed neoplasia that reports a DNA methylation value below the threshold value (e.g., the range associated with no disease). The value of sensitivity, therefore, reflects the probability that a DNA methylation measurement for a given marker obtained from a known diseased sample will be in the range of disease-associated measurements. As defined here, the clinical relevance of the calculated sensitivity value represents an estimation of the probability that a given marker would detect the presence of a clinical condition when applied to a subject with that condition.

As used herein, the “specificity” of a given marker refers to the percentage of non-neoplastic samples that report a DNA methylation value below a threshold value that distinguishes between neoplastic and non-neoplastic samples. In some embodiments, a negative is defined as a histology-confirmed non-neoplastic sample that reports a DNA methylation value below the threshold value (e.g., the range associated with no disease) and a false positive is defined as a histology-confirmed non-neoplastic sample that reports a DNA methylation value above the threshold value (e.g., the range associated with disease). The value of specificity, therefore, reflects the probability that a DNA methylation measurement for a given marker obtained from a known non-neoplastic sample will be in the range of non-disease associated measurements. As defined here, the clinical relevance of the calculated specificity value represents an estimation of the probability that a given marker would detect the absence of a clinical condition when applied to a patient without that condition.

The term “AUC” as used herein is an abbreviation for the “area under a curve”. In particular it refers to the area under a Receiver Operating Characteristic (ROC) curve. The ROC curve is a plot of the true positive rate against the false positive rate for the different possible cut points of a diagnostic test. It shows the trade-off between sensitivity and specificity depending on the selected cut point (any increase in sensitivity will be accompanied by a decrease in specificity). The area under an ROC curve (AUC) is a measure for the accuracy of a diagnostic test (the larger the area the better; the optimum is 1: a random test would have a ROC curve lying on the diagonal with an area of 0.5; for reference: J. P. Egan. (1975) Signal Detection Theory and ROC Analysis, Academic Press, New York).

As used herein, the term “neoplasm” refers to “an abnormal mass of tissue, the growth of which exceeds and is uncoordinated with that of the normal tissues” See, e.g., Willis R A, “The Spread of Tumors in the Human Body”, London, Butterworth & Co, 1952.

As used herein, the term “adenoma” refers to a benign tumor of glandular origin. Although these growths are benign, over time they may progress to become malignant.

The term “pre-cancerous” or “pre-neoplastic” and equivalents thereof refer to any cellular proliferative disorder that is undergoing malignant transformation.

A “site” of a neoplasm, adenoma, cancer, etc. is the tissue, organ, cell type, anatomical area, body part, etc. in a subject's body where the neoplasm, adenoma, cancer, etc. is located.

As used herein, a “diagnostic” test application includes the detection or identification of a disease state or condition of a subject, determining the likelihood that a subject will contract a given disease or condition, determining the likelihood that a subject with a disease or condition will respond to therapy, determining the prognosis of a subject with a disease or condition (or its likely progression or regression), and determining the effect of a treatment on a subject with a disease or condition. For example, a diagnostic can be used for detecting the presence or likelihood of a subject contracting a neoplasm or the likelihood that such a subject will respond favorably to a compound (e.g., a pharmaceutical, e.g., a drug) or other treatment.

The term “marker”, as used herein, refers to a substance (e.g., a nucleic acid or a region of a nucleic acid) that is able to diagnose a cancer by distinguishing cancerous cells from normal cells, e.g., based its methylation state.

The term “isolated” when used in relation to a nucleic acid, as in “an isolated oligonucleotide” refers to a nucleic acid sequence that is identified and separated from at least one contaminant nucleic acid with which it is ordinarily associated in its natural source. Isolated nucleic acid is present in a form or setting that is different from that in which it is found in nature. In contrast, non-isolated nucleic acids, such as DNA and RNA, are found in the state they exist in nature. Examples of non-isolated nucleic acids include: a given DNA sequence (e.g., a gene) found on the host cell chromosome in proximity to neighboring genes; RNA sequences, such as a specific mRNA sequence encoding a specific protein, found in the cell as a mixture with numerous other mRNAs which encode a multitude of proteins. However, isolated nucleic acid encoding a particular protein includes, by way of example, such nucleic acid in cells ordinarily expressing the protein, where the nucleic acid is in a chromosomal location different from that of natural cells, or is otherwise flanked by a different nucleic acid sequence than that found in nature. The isolated nucleic acid or oligonucleotide may be present in single-stranded or double-stranded form. When an isolated nucleic acid or oligonucleotide is to be utilized to express a protein, the oligonucleotide will contain at a minimum the sense or coding strand (i.e., the oligonucleotide may be single-stranded), but may contain both the sense and anti-sense strands (i.e., the oligonucleotide may be double-stranded). An isolated nucleic acid may, after isolation from its natural or typical environment, by be combined with other nucleic acids or molecules. For example, an isolated nucleic acid may be present in a host cell in which into which it has been placed, e.g., for heterologous expression.

The term “purified” refers to molecules, either nucleic acid or amino acid sequences that are removed from their natural environment, isolated, or separated. An “isolated nucleic acid sequence” may therefore be a purified nucleic acid sequence. “Substantially purified” molecules are at least 60% free, preferably at least 75% free, and more preferably at least 90% free from other components with which they are naturally associated. As used herein, the terms “purified” or “to purify” also refer to the removal of contaminants from a sample. The removal of contaminating proteins results in an increase in the percent of polypeptide or nucleic acid of interest in the sample. In another example, recombinant polypeptides are expressed in plant, bacterial, yeast, or mammalian host cells and the polypeptides are purified by the removal of host cell proteins; the percent of recombinant polypeptides is thereby increased in the sample.

The term “composition comprising” a given polynucleotide sequence or polypeptide refers broadly to any composition containing the given polynucleotide sequence or polypeptide. The composition may comprise an aqueous solution containing salts (e.g., NaCl), detergents (e.g., SDS), and other components (e.g., Denhardt's solution, dry milk, salmon sperm DNA, etc.).

The term “sample” is used in its broadest sense. In one sense it can refer to an animal cell or tissue. In another sense, it is meant to include a specimen or culture obtained from any source, as well as biological and environmental samples. Biological samples may be obtained from plants or animals (including humans) and encompass fluids, solids, tissues, and gases. Environmental samples include environmental material such as surface matter, soil, water, and industrial samples. These examples are not to be construed as limiting the sample types applicable to the present invention.

As used herein, a “remote sample” as used in some contexts relates to a sample indirectly collected from a site that is not the cell, tissue, or organ source of the sample. For instance, when sample material originating from the pancreas is assessed in a stool sample (e.g., not from a sample taken directly from a pancreas), the sample is a remote sample.

As used herein, the terms “patient” or “subject” refer to organisms to be subject to various tests provided by the technology. The term “subject” includes animals, preferably mammals, including humans. In a preferred embodiment, the subject is a primate. In an even more preferred embodiment, the subject is a human.

As used herein, the term “kit” refers to any delivery system for delivering materials. In the context of reaction assays, such delivery systems include systems that allow for the storage, transport, or delivery of reaction reagents (e.g., oligonucleotides, enzymes, etc. in the appropriate containers) and/or supporting materials (e.g., buffers, written instructions for performing the assay etc.) from one location to another. For example, kits include one or more enclosures (e.g., boxes) containing the relevant reaction reagents and/or supporting materials. As used herein, the term “fragmented kit” refers to delivery systems comprising two or more separate containers that each contain a subportion of the total kit components. The containers may be delivered to the intended recipient together or separately. For example, a first container may contain an enzyme for use in an assay, while a second container contains oligonucleotides. The term “fragmented kit” is intended to encompass kits containing Analyte specific reagents (ASR's) regulated under section 520(e) of the Federal Food, Drug, and Cosmetic Act, but are not limited thereto. Indeed, any delivery system comprising two or more separate containers that each contains a subportion of the total kit components are included in the term “fragmented kit.” In contrast, a “combined kit” refers to a delivery system containing all of the components of a reaction assay in a single container (e.g., in a single box housing each of the desired components). The term “kit” includes both fragmented and combined kits.

Embodiments of the Technology

Provided herein is technology for pancreatic cancer screening markers and other gastrointestinal cancer screening markers that provide a high signal-to-noise ratio and a low background level when detected from samples taken from a subject (e.g., stool sample). Markers were identified in a case-control study by comparing the methylation state of DNA markers from tumors of subjects with stage I and stage II PanC to the methylation state of the same DNA markers from control subjects (e.g., normal tissue such as normal colon and/or non-neoplastic pancreas) (see, Examples 1 and 11).

Markers and/or panels of markers (e.g., a chromosomal region having an annotation selected from ABCB1, ADCY1, BHLHE23 (LOC63930), c13orf18, CACNA1C, chr12 133, CLEC11A, ELMO1, EOMES, CLEC 11, SHH, GJC1, IHIF1, IKZF1, KCNK12, KCNN2, PCBP3, PRKCB, RSPO3, SCARF2, SLC38A3, ST8SIA1, TWIST1, VWC2, WT1, and ZNF71) were identified in a case-control study by comparing the methylation state of DNA markers (e.g., from tumors of subjects with stage I and stage II PanC to the methylation state of the same DNA markers from control subjects (e.g., normal tissue such as normal colon and/or non-neoplastic pancreas) (see, Examples 2 and 8).

Markers and/or panels of markers (e.g., a chromosomal region having an annotation selected from NDRG4, SFRP1, BMP3, HPP1, and/or APC) were identified in case-control studies by comparing the methylation state of DNA markers from esophageal tissue of subjects with Barrett's esophagus to the methylation state of the same DNA markers from control subjects (see, Examples 4 and 10).

Markers and/or panels of markers (e.g., a chromosomal region having an annotation selected from ADCY1, PRKCB, KCNK12, C13ORF18, IKZF1, TWIST1, ELMO, 55957, CD1D, CLEC11A, KCNN2, BMP3, and/or NDRG4) were identified in case-control studies by comparing the methylation state of DNA markers from a pancreatic juice sample from subjects with pancreas cancer to the methylation state of the same DNA markers from control subjects (see, Examples 5 and 6).

A marker (e.g., a chromosomal region having a CD1D annotation) was identified in a case-control study by comparing the methylation state of a DNA marker (e.g., CD1D) from a stool sample from subjects with pancreas cancer to the methylation state of the same DNA marker from control subjects not having pancreas cancer (see, Example 7).

A marker (e.g., miR-1290) was identified in a case-control study by comparing the quantitated amount of a DNA marker (e.g., miR-1290) from a stool sample from subjects with pancreas cancer to the quantitated amount of of the same DNA marker from control subjects not having pancreas cancer (see, Example 9).

In addition, the technology provides various panels of markers, e.g., in some embodiments the marker comprises a chromosomal region having an annotation that is ABCB1, ADCY1, BHLHE23 (LOC63930), c13orf18, CACNA1C, chr12.133, CLEC11A, ELMO1, EOMES, GJC1, IHIF1, IKZF1, KCNK12, KCNN2, NDRG4, PCBP3, PRKCB, RSPO3, SCARF2, SLC38A3, ST8SIA1, TWIST1, VWC2, WT1, or ZNF71, and that comprises the marker (see, Tables 1 and 9). In addition, embodiments provide a method of analyzing a DMR from Table 1 that is DMR No. 11, 14, 15, 65, 21, 22, 23, 5, 29, 30, 38, 39, 41, 50, 51, 55, 57, 60, 61, 8, 75, 81, 82, 84, 87, 93, 94, 98, 99, 103, 104, or 107, and/or a DMR corresponding to Chr16:58497395-58497458. Some embodiments provide determining the methylation state of a marker, wherein a chromosomal region having an annotation that is CLEC11A, C13ORF18, KCNN2, ABCB1, SLC38A3, CD1D, IKZF1, ADCY1, CHR12133, RSPO3, MBP3, PRKCB, NDRG4, ELMO, or TWIST1 comprises the marker. In some embodiments, the methods comprise determining the methylation state of two markers, e.g., a pair of markers provided in a row of Table 5.

Although the disclosure herein refers to certain illustrated embodiments, it is to be understood that these embodiments are presented by way of example and not by way of limitation.

In particular aspects, the present technology provides compositions and methods for identifying, determining, and/or classifying a cancer such as an upper gastrointestinal cancer (e.g., cancer of the esophagus, pancreas, stomach) or lower gastrointestinal cancer (e.g., adenoma, colorectal cancer). In related aspects, the technology provides compositions and methods for identifying, predicting, and/or detecting the site of a cancer. The methods comprise determining the methylation status of at least one methylation marker in a biological sample isolated from a subject, wherein a change in the methylation state of the marker is indicative of the presence, class, or site of a cancer. Particular embodiments relate to markers comprising a differentially methylated region (DMR, e.g., DMR 1-107, see Table 1, e.g., DMR 1-449, see Table 10) that are used for diagnosis (e.g., screening) of neoplastic cellular proliferative disorders (e.g., cancer), including early detection during the pre-cancerous stages of disease and prediction of a neoplasm site (e.g., by discriminating among cancer types, e.g., upper gastrointestinal cancers and lower gastrointestinal cancers). Furthermore, the markers are used for the differentiation of neoplastic from benign cellular proliferative disorders. In particular aspects, the present technology discloses a method wherein a neoplastic cell proliferative disorder is distinguished from a benign cell proliferative disorder.

The markers of the present technology are particularly efficient in detecting or distinguishing between colorectal and pancreatic proliferative disorders, thereby providing improved means for the early detection, classification, and treatment of said disorders.

In addition to embodiments wherein the methylation analysis of at least one marker, a region of a marker, or a base of a marker comprising a DMR (e.g., DMR 1-107 from Table 1) (e.g., DMR 1-449 from Table 10) provided herein and listed in Table 1 or 10 is analyzed, the technology also provides panels of markers comprising at least one marker, region of a marker, or base of a marker comprising a DMR with utility for the detection of cancers, in particular colorectal, pancreatic cancer, and other upper and lower GI cancers.

Some embodiments of the technology are based upon the analysis of the CpG methylation status of at least one marker, region of a marker, or base of a marker comprising a DMR.

In some embodiments, the present technology provides for the use of the bisulfite technique in combination with one or more methylation assays to determine the methylation status of CpG dinucleotide sequences within at least one marker comprising a DMR (e.g., as provided in Table 1 (e.g., DMR 1-107)) (e.g., as provided in Table 10 (e.g., DMR 1-449)). Genomic CpG dinucleotides can be methylated or unmethylated (alternatively known as up- and down-methylated respectively). However the methods of the present invention are suitable for the analysis of biological samples of a heterogeneous nature, e.g., a low concentration of tumor cells, or biological materials therefrom, within a background of a remote sample (e.g., blood, organ effluent, or stool). Accordingly, when analyzing the methylation status of a CpG position within such a sample one may use a quantitative assay for determining the level (e.g., percent, fraction, ratio, proportion, or degree) of methylation at a particular CpG position.

According to the present technology, determination of the methylation status of CpG dinucleotide sequences in markers comprising a DMR has utility both in the diagnosis and characterization of cancers such as upper gastrointestinal cancer (e.g., cancer of the esophagus, pancreas, stomach) or lower gastrointestinal cancer (e.g., adenoma, colorectal cancer).

Combinations of Markers

In some embodiments, the technology relates to assessing the methylation state of combinations of markers comprising a DMR from Table 1 (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 27, 29, 30) or Table 10 (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 27, 29, 30), or more markers comprising a DMR. In some embodiments, assessing the methylation state of more than one marker increases the specificity and/or sensitivity of a screen or diagnostic for identifying a neoplasm in a subject, e.g., an upper gastrointestinal cancer (e.g., esophagus, pancreas, stomach) or a lower gastrointestinal cancer (e.g., adenoma, colorectal). In some embodiments, a marker or a combination of markers discriminates between types and/or locations of a neoplasm. For example, combinations of markers discriminate esophageal neoplasm, stomach neoplasm, pancreatic neoplasm, colorectal neoplasm, and adenomas from each other, from other neoplasms, and/or from normal (e.g., non-cancerous, non-precancerous) tissue.

Various cancers are predicted by various combinations of markers, e.g., as identified by statistical techniques related to specificity and sensitivity of prediction. The technology provides methods for identifying predictive combinations and validated predictive combinations for some cancers.

In some embodiments, combinations of markers (e.g., comprising a DMR) predict the site of a neoplasm. For example, during the development of the technology described herein, statistical analyses were performed to validate the sensitivity and specificity of marker combinations. For example, marker pairs accurately predicted tumor site in >90% of samples, the top 17 marker pairs accurately predicted tumor site in >80% of samples, and the top 49 marker pairs accurately predicted tumor site in 70% of the samples.

Methods for Assaying Methylation State

The most frequently used method for analyzing a nucleic acid for the presence of 5-methylcytosine is based upon the bisulfite method described by Frommer, et al. for the detection of 5-methylcytosines in DNA (Frommer et al. (1992) Proc. Natl. Acad. Sci. USA 89: 1827-31 explicitly incorporated herein by reference in its entirety for all purposes) or variations thereof. The bisulfite method of mapping 5-methylcytosines is based on the observation that cytosine, but not 5-methylcytosine, reacts with hydrogen sulfite ion (also known as bisulfite). The reaction is usually performed according to the following steps: first, cytosine reacts with hydrogen sulfite to form a sulfonated cytosine. Next, spontaneous deamination of the sulfonated reaction intermediate results in a sulfonated uracil. Finally, the sulfonated uricil is desulfonated under alkaline conditions to form uracil. Detection is possible because uracil forms base pairs with adenine (thus behaving like thymine), whereas 5-methylcytosine base pairs with guanine (thus behaving like cytosine). This makes the discrimination of methylated cytosines from non-methylated cytosines possible by, e.g., bisulfite genomic sequencing (Grigg G, & Clark S, Bioessays (1994) 16: 431-36; Grigg G, DNA Seq. (1996) 6: 189-98) or methylation-specific PCR (MSP) as is disclosed, e.g., in U.S. Pat. No. 5,786,146.

Some conventional technologies are related to methods comprising enclosing the DNA to be analyzed in an agarose matrix, thereby preventing the diffusion and renaturation of the DNA (bisulfite only reacts with single-stranded DNA), and replacing precipitation and purification steps with a fast dialysis (Olek A, et al. (1996) “A modified and improved method for bisulfite based cytosine methylation analysis” Nucleic Acids Res. 24: 5064-6). It is thus possible to analyze individual cells for methylation status, illustrating the utility and sensitivity of the method. An overview of conventional methods for detecting 5-methylcytosine is provided by Rein, T., et al. (1998) Nucleic Acids Res. 26: 2255.

The bisulfite technique typically involves amplifying short, specific fragments of a known nucleic acid subsequent to a bisulfite treatment, then either assaying the product by sequencing (Olek & Walter (1997) Nat. Genet. 17: 275-6) or a primer extension reaction (Gonzalgo & Jones (1997) Nucleic Acids Res. 25: 2529-31; WO 95/00669; U.S. Pat. No. 6,251,594) to analyze individual cytosine positions. Some methods use enzymatic digestion (Xiong & Laird (1997) Nucleic Acids Res. 25: 2532-4). Detection by hybridization has also been described in the art (Olek et al., WO 99/28498). Additionally, use of the bisulfite technique for methylation detection with respect to individual genes has been described (Grigg & Clark (1994) Bioessays 16: 431-6; Zeschnigk et al. (1997) Hum Mol Genet. 6: 387-95; Feil et al. (1994) Nucleic Acids Res. 22: 695; Martin et al. (1995) Gene 157: 261-4; WO 9746705; WO 9515373).

Various methylation assay procedures are known in the art and can be used in conjunction with bisulfite treatment according to the present technology. These assays allow for determination of the methylation state of one or a plurality of CpG dinucleotides (e.g., CpG islands) within a nucleic acid sequence. Such assays involve, among other techniques, sequencing of bisulfite-treated nucleic acid, PCR (for sequence-specific amplification), Southern blot analysis, and use of methylation-sensitive restriction enzymes.

For example, genomic sequencing has been simplified for analysis of methylation patterns and 5-methylcytosine distributions by using bisulfite treatment (Frommer et al. (1992) Proc. Natl. Acad. Sci. USA 89: 1827-1831). Additionally, restriction enzyme digestion of PCR products amplified from bisulfite-converted DNA finds use in assessing methylation state, e.g., as described by Sadri & Hornsby (1997) Nucl. Acids Res. 24: 5058-5059 or as embodied in the method known as COBRA (Combined Bisulfite Restriction Analysis) (Xiong & Laird (1997) Nucleic Acids Res. 25: 2532-2534).

COBRA™ analysis is a quantitative methylation assay useful for determining DNA methylation levels at specific loci in small amounts of genomic DNA (Xiong & Laird, Nucleic Acids Res. 25:2532-2534, 1997). Briefly, restriction enzyme digestion is used to reveal methylation-dependent sequence differences in PCR products of sodium bisulfite-treated DNA. Methylation-dependent sequence differences are first introduced into the genomic DNA by standard bisulfite treatment according to the procedure described by Frommer et al. (Proc. Natl. Acad. Sci. USA 89:1827-1831, 1992). PCR amplification of the bisulfite converted DNA is then performed using primers specific for the CpG islands of interest, followed by restriction endonuclease digestion, gel electrophoresis, and detection using specific, labeled hybridization probes. Methylation levels in the original DNA sample are represented by the relative amounts of digested and undigested PCR product in a linearly quantitative fashion across a wide spectrum of DNA methylation levels. In addition, this technique can be reliably applied to DNA obtained from microdissected paraffin-embedded tissue samples.

Typical reagents (e.g., as might be found in a typical COBRA™-based kit) for COBRA™ analysis may include, but are not limited to: PCR primers for specific loci (e.g., specific genes, markers, DMR, regions of genes, regions of markers, bisulfite treated DNA sequence, CpG island, etc.); restriction enzyme and appropriate buffer; gene-hybridization oligonucleotide; control hybridization oligonucleotide; kinase labeling kit for oligonucleotide probe; and labeled nucleotides. Additionally, bisulfite conversion reagents may include: DNA denaturation buffer; sulfonation buffer; DNA recovery reagents or kits (e.g., precipitation, ultrafiltration, affinity column); desulfonation buffer; and DNA recovery components.

Preferably, assays such as “MethyLight™” (a fluorescence-based real-time PCR technique) (Eads et al., Cancer Res. 59:2302-2306, 1999), Ms-SNuPE™ (Methylation-sensitive Single Nucleotide Primer Extension) reactions (Gonzalgo & Jones, Nucleic Acids Res. 25:2529-2531, 1997), methylation-specific PCR (“MSP”; Herman et al., Proc. Natl. Acad. Sci. USA 93:9821-9826, 1996; U.S. Pat. No. 5,786,146), and methylated CpG island amplification (“MCA”; Toyota et al., Cancer Res. 59:2307-12, 1999) are used alone or in combination with one or more of these methods.

The “HeavyMethyl™” assay, technique is a quantitative method for assessing methylation differences based on methylation-specific amplification of bisulfite-treated DNA. Methylation-specific blocking probes (“blockers”) covering CpG positions between, or covered by, the amplification primers enable methylation-specific selective amplification of a nucleic acid sample.

The term “HeavyMethyl™ MethyLight™” assay refers to a HeavyMethyl™ MethyLight™ assay, which is a variation of the MethyLight™ assay, wherein the MethyLight™ assay is combined with methylation specific blocking probes covering CpG positions between the amplification primers. The HeavyMethyl™ assay may also be used in combination with methylation specific amplification primers.

Typical reagents (e.g., as might be found in a typical MethyLight™-based kit) for HeavyMethyl™ analysis may include, but are not limited to: PCR primers for specific loci (e.g., specific genes, markers, DMR, regions of genes, regions of markers, bisulfite treated DNA sequence, CpG island, or bisulfite treated DNA sequence or CpG island, etc.); blocking oligonucleotides; optimized PCR buffers and deoxynucleotides; and Taq polymerase.

MSP (methylation-specific PCR) allows for assessing the methylation status of virtually any group of CpG sites within a CpG island, independent of the use of methylation-sensitive restriction enzymes (Herman et al. Proc. Natl. Acad. Sci. USA 93:9821-9826, 1996; U.S. Pat. No. 5,786,146). Briefly, DNA is modified by sodium bisulfite, which converts unmethylated, but not methylated cytosines, to uracil, and the products are subsequently amplified with primers specific for methylated versus unmethylated DNA. MSP requires only small quantities of DNA, is sensitive to 0.1% methylated alleles of a given CpG island locus, and can be performed on DNA extracted from paraffin-embedded samples. Typical reagents (e.g., as might be found in a typical MSP-based kit) for MSP analysis may include, but are not limited to: methylated and unmethylated PCR primers for specific loci (e.g., specific genes, markers, DMR, regions of genes, regions of markers, bisulfite treated DNA sequence, CpG island, etc.); optimized PCR buffers and deoxynucleotides, and specific probes.

The MethyLight™ assay is a high-throughput quantitative methylation assay that utilizes fluorescence-based real-time PCR (e.g., TaqMan) that requires no further manipulations after the PCR step (Eads et al., Cancer Res. 59:2302-2306, 1999). Briefly, the MethyLight™ process begins with a mixed sample of genomic DNA that is converted, in a sodium bisulfite reaction, to a mixed pool of methylation-dependent sequence differences according to standard procedures (the bisulfite process converts unmethylated cytosine residues to uracil). Fluorescence-based PCR is then performed in a “biased” reaction, e.g., with PCR primers that overlap known CpG dinucleotides. Sequence discrimination occurs both at the level of the amplification process and at the level of the fluorescence detection process.

The MethyLight™ assay is used as a quantitative test for methylation patterns in a nucleic acid, e.g., a genomic DNA sample, wherein sequence discrimination occurs at the level of probe hybridization. In a quantitative version, the PCR reaction provides for a methylation specific amplification in the presence of a fluorescent probe that overlaps a particular putative methylation site. An unbiased control for the amount of input DNA is provided by a reaction in which neither the primers, nor the probe, overlie any CpG dinucleotides. Alternatively, a qualitative test for genomic methylation is achieved by probing the biased PCR pool with either control oligonucleotides that do not cover known methylation sites (e.g., a fluorescence-based version of the HeavyMethyl™ and MSP techniques) or with oligonucleotides covering potential methylation sites.

The MethyLight™ process is used with any suitable probe (e.g. a “TaqMan®” probe, a Lightcycler® probe, etc.) For example, in some applications double-stranded genomic DNA is treated with sodium bisulfite and subjected to one of two sets of PCR reactions using TaqMan® probes, e.g., with MSP primers and/or HeavyMethyl blocker oligonucleotides and a TaqMan® probe. The TaqMan® probe is dual-labeled with fluorescent “reporter” and “quencher” molecules and is designed to be specific for a relatively high GC content region so that it melts at about a 10° C. higher temperature in the PCR cycle than the forward or reverse primers. This allows the TaqMan® probe to remain fully hybridized during the PCR annealing/extension step. As the Taq polymerase enzymatically synthesizes a new strand during PCR, it will eventually reach the annealed TaqMan® probe. The Taq polymerase 5′ to 3′ endonuclease activity will then displace the TaqMan® probe by digesting it to release the fluorescent reporter molecule for quantitative detection of its now unquenched signal using a real-time fluorescent detection system.

Typical reagents (e.g., as might be found in a typical MethyLight™-based kit) for MethyLight™ analysis may include, but are not limited to: PCR primers for specific loci (e.g., specific genes, markers, DMR, regions of genes, regions of markers, bisulfite treated DNA sequence, CpG island, etc.); TaqMan® or Lightcycler® probes; optimized PCR buffers and deoxynucleotides; and Taq polymerase.

The QM™ (quantitative methylation) assay is an alternative quantitative test for methylation patterns in genomic DNA samples, wherein sequence discrimination occurs at the level of probe hybridization. In this quantitative version, the PCR reaction provides for unbiased amplification in the presence of a fluorescent probe that overlaps a particular putative methylation site. An unbiased control for the amount of input DNA is provided by a reaction in which neither the primers, nor the probe, overlie any CpG dinucleotides. Alternatively, a qualitative test for genomic methylation is achieved by probing the biased PCR pool with either control oligonucleotides that do not cover known methylation sites (a fluorescence-based version of the HeavyMethyl™ and MSP techniques) or with oligonucleotides covering potential methylation sites.

The QM™ process can by used with any suitable probe, e.g., “TaqMan®” probes, Lightcycler® probes, in the amplification process. For example, double-stranded genomic DNA is treated with sodium bisulfite and subjected to unbiased primers and the TaqMan® probe. The TaqMan® probe is dual-labeled with fluorescent “reporter” and “quencher” molecules, and is designed to be specific for a relatively high GC content region so that it melts out at about a 10° C. higher temperature in the PCR cycle than the forward or reverse primers. This allows the TaqMan® probe to remain fully hybridized during the PCR annealing/extension step. As the Taq polymerase enzymatically synthesizes a new strand during PCR, it will eventually reach the annealed TaqMan® probe. The Taq polymerase 5′ to 3′ endonuclease activity will then displace the TaqMan® probe by digesting it to release the fluorescent reporter molecule for quantitative detection of its now unquenched signal using a real-time fluorescent detection system. Typical reagents (e.g., as might be found in a typical QMT-based kit) for QM™ analysis may include, but are not limited to: PCR primers for specific loci (e.g., specific genes, markers, DMR, regions of genes, regions of markers, bisulfite treated DNA sequence, CpG island, etc.); TaqMan® or Lightcycler® probes; optimized PCR buffers and deoxynucleotides; and Taq polymerase.

The Ms-SNuPE™ technique is a quantitative method for assessing methylation differences at specific CpG sites based on bisulfite treatment of DNA, followed by single-nucleotide primer extension (Gonzalgo & Jones, Nucleic Acids Res. 25:2529-2531, 1997). Briefly, genomic DNA is reacted with sodium bisulfite to convert unmethylated cytosine to uracil while leaving 5-methylcytosine unchanged. Amplification of the desired target sequence is then performed using PCR primers specific for bisulfite-converted DNA, and the resulting product is isolated and used as a template for methylation analysis at the CpG site of interest. Small amounts of DNA can be analyzed (e.g., microdissected pathology sections) and it avoids utilization of restriction enzymes for determining the methylation status at CpG sites.

Typical reagents (e.g., as might be found in a typical Ms-SNuPE™-based kit) for Ms-SNuPE™ analysis may include, but are not limited to: PCR primers for specific loci (e.g., specific genes, markers, DMR, regions of genes, regions of markers, bisulfite treated DNA sequence, CpG island, etc.); optimized PCR buffers and deoxynucleotides; gel extraction kit; positive control primers; Ms-SNuPE™ primers for specific loci; reaction buffer (for the Ms-SNuPE reaction); and labeled nucleotides. Additionally, bisulfite conversion reagents may include: DNA denaturation buffer; sulfonation buffer; DNA recovery reagents or kit (e.g., precipitation, ultrafiltration, affinity column); desulfonation buffer; and DNA recovery components.

Reduced Representation Bisulfite Sequencing (RRBS) begins with bisulfite treatment of nucleic acid to convert all unmethylated cytosines to uracil, followed by restriction enzyme digestion (e.g., by an enzyme that recognizes a site including a CG sequence such as MspI) and complete sequencing of fragments after coupling to an adapter ligand. The choice of restriction enzyme enriches the fragments for CpG dense regions, reducing the number of redundant sequences that may map to multiple gene positions during analysis. As such, RRBS reduces the complexity of the nucleic acid sample by selecting a subset (e.g., by size selection using preparative gel electrophoresis) of restriction fragments for sequencing. As opposed to whole-genome bisulfite sequencing, every fragment produced by the restriction enzyme digestion contains DNA methylation information for at least one CpG dinucleotide. As such, RRBS enriches the sample for promoters, CpG islands, and other genomic features with a high frequency of restriction enzyme cut sites in these regions and thus provides an assay to assess the methylation state of one or more genomic loci.

A typical protocol for RRBS comprises the steps of digesting a nucleic acid sample with a restriction enzyme such as MspI, filling in overhangs and A-tailing, ligating adaptors, bisulfite conversion, and PCR. See, e.g., et al. (2005) “Genome-scale DNA methylation mapping of clinical samples at single-nucleotide resolution” Nat Methods 7: 133-6; Meissner et al. (2005) “Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis” Nucleic Acids Res. 33: 5868-77.

In some embodiments, a quantitative allele-specific real-time target and signal amplification (QuARTS) assay is used to evaluate methylation state. Three reactions sequentially occur in each QuARTS assay, including amplification (reaction 1) and target probe cleavage (reaction 2) in the primary reaction; and FRET cleavage and fluorescent signal generation (reaction 3) in the secondary reaction. When target nucleic acid is amplified with specific primers, a specific detection probe with a flap sequence loosely binds to the amplicon. The presence of the specific invasive oligonucleotide at the target binding site causes cleavase to release the flap sequence by cutting between the detection probe and the flap sequence. The flap sequence is complementary to a nonhairpin portion of a corresponding FRET cassette. Accordingly, the flap sequence functions as an invasive oligonucleotide on the FRET cassette and effects a cleavage between the FRET cassette fluorophore and a quencher, which produces a fluorescent signal. The cleavage reaction can cut multiple probes per target and thus release multiple fluorophore per flap, providing exponential signal amplification. QuARTS can detect multiple targets in a single reaction well by using FRET cassettes with different dyes. See, e.g., in Zou et al. (2010) “Sensitive quantification of methylated markers with a novel methylation specific technology” Clin Chem 56: A199; U.S. patent application Ser. Nos. 12/946,737, 12/946,745, 12/946,752, and 61/548,639.

The term “bisulfite reagent” refers to a reagent comprising bisulfite, disulfite, hydrogen sulfite, or combinations thereof, useful as disclosed herein to distinguish between methylated and unmethylated CpG dinucleotide sequences. Methods of said treatment are known in the art (e.g., PCT/EP2004/011715, which is incorporated by reference in its entirety). It is preferred that the bisulfite treatment is conducted in the presence of denaturing solvents such as but not limited to n-alkylenglycol or diethylene glycol dimethyl ether (DME), or in the presence of dioxane or dioxane derivatives. In some embodiments the denaturing solvents are used in concentrations between 1% and 35% (v/v). In some embodiments, the bisulfite reaction is carried out in the presence of scavengers such as but not limited to chromane derivatives, e.g., 6-hydroxy-2,5,7,8,-tetramethylchromane 2-carboxylic acid or trihydroxybenzone acid and derivates thereof, e.g., Gallic acid (see: PCT/EP2004/011715, which is incorporated by reference in its entirety). The bisulfite conversion is preferably carried out at a reaction temperature between 30° C. and 70° C., whereby the temperature is increased to over 85° C. for short times during the reaction (see: PCT/EP2004/011715, which is incorporated by reference in its entirety). The bisulfite treated DNA is preferably purified prior to the quantification. This may be conducted by any means known in the art, such as but not limited to ultrafiltration, e.g., by means of Microcon™ columns (manufactured by Millipore™). The purification is carried out according to a modified manufacturer's protocol (see, e.g., PCT/EP2004/011715, which is incorporated by reference in its entirety).

In some embodiments, fragments of the treated DNA are amplified using sets of primer oligonucleotides according to the present invention (e.g., see Table 2) and an amplification enzyme. The amplification of several DNA segments can be carried out simultaneously in one and the same reaction vessel. Typically, the amplification is carried out using a polymerase chain reaction (PCR). Amplicons are typically 100 to 2000 base pairs in length.

In another embodiment of the method, the methylation status of CpG positions within or near a marker comprising a DMR (e.g., DMR 1-107 as provided in Table 1) (e.g., DMR 1-449 as provided in Table 10) may be detected by use of methylation-specific primer oligonucleotides. This technique (MSP) has been described in U.S. Pat. No. 6,265,171 to Herman. The use of methylation status specific primers for the amplification of bisulfite treated DNA allows the differentiation between methylated and unmethylated nucleic acids. MSP primer pairs contain at least one primer that hybridizes to a bisulfite treated CpG dinucleotide. Therefore, the sequence of said primers comprises at least one CpG dinucleotide. MSP primers specific for non-methylated DNA contain a “T” at the position of the C position in the CpG.

The fragments obtained by means of the amplification can carry a directly or indirectly detectable label. In some embodiments, the labels are fluorescent labels, radionuclides, or detachable molecule fragments having a typical mass that can be detected in a mass spectrometer. Where said labels are mass labels, some embodiments provide that the labeled amplicons have a single positive or negative net charge, allowing for better delectability in the mass spectrometer. The detection may be carried out and visualized by means of, e.g., matrix assisted laser desorption/ionization mass spectrometry (MALDI) or using electron spray mass spectrometry (ESI).

Methods for isolating DNA suitable for these assay technologies are known in the art. In particular, some embodiments comprise isolation of nucleic acids as described in U.S. patent application Ser. No. 13/470,251 (“Isolation of Nucleic Acids”), incorporated herein by reference in its entirety.

Methods

In some embodiments the technology, methods are provided that comprise the following steps:

-   -   1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated         from a body fluids such as a stool sample or pancreatic tissue)         obtained from the subject with at least one reagent or series of         reagents that distinguishes between methylated and         non-methylated CpG dinucleotides within at least one marker         comprising a DMR (e.g., DMR 1-107, e.g., as provided in Table 1)         (e.g., DMR 1-449, e.g., as provided in Table 10) and     -   2) detecting a neoplasm or proliferative disorder (e.g.,         afforded with a sensitivity of greater than or equal to 80% and         a specificity of greater than or equal to 80%).

In some embodiments the technology, methods are provided that comprise the following steps:

-   -   1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated         from a body fluids such as a stool sample or pancreatic tissue)         obtained from the subject with at least one reagent or series of         reagents that distinguishes between methylated and         non-methylated CpG dinucleotides within at least one marker         selected from a chromosomal region having an annotation selected         from the group consisting of ABCB1, ADCY1, BHLHE23 (LOC63930),         c13orf18, CACNA1C, chr12 133, CLEC11A, ELMO1, EOMES, CLEC 11,         SHH, GJC1, IHIF1, IKZF1, KCNK12, KCNN2, PCBP3, PRKCB, RSPO3,         SCARF2, SLC38A3, ST8SIA1, TWIST1, VWC2, WT1, and ZNF71, and     -   2) detecting pancreatic cancer (e.g., afforded with a         sensitivity of greater than or equal to 80% and a specificity of         greater than or equal to 80%).

In some embodiments the technology, methods are provided that comprise the following steps:

-   -   1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated         from a body fluids such as a stool sample or esophageal tissue)         obtained from the subject with at least one reagent or series of         reagents that distinguishes between methylated and         non-methylated CpG dinucleotides within at least one marker         selected from a chromosomal region having an annotation selected         from the group consisting of NDRG4, SFRP1, BMP3, HPP1, and APC,         and     -   2) detecting Barrett's esophagus (e.g., afforded with a         sensitivity of greater than or equal to 80% and a specificity of         greater than or equal to 80%).

In some embodiments the technology, methods are provided that comprise the following steps:

-   -   1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated         from a body fluids such as a stool sample or pancreatic tissue)         obtained from the subject with at least one reagent or series of         reagents that distinguishes between methylated and         non-methylated CpG dinucleotides within at least one marker         selected from a chromosomal region having an annotation selected         from the group consisting of ADCY1, PRKCB, KCNK12, C13ORF18,         IKZF1, TWIST1, ELMO, 55957, CD1D, CLEC11A, KCNN2, BMP3, and         NDRG4, and     -   2) detecting pancreatic cancer (e.g., afforded with a         sensitivity of greater than or equal to 80% and a specificity of         greater than or equal to 80%).

In some embodiments the technology, methods are provided that comprise the following steps:

-   -   1) contacting a nucleic acid (e.g., genomic DNA, e.g., isolated         from a stool sample) obtained from the subject with at least one         reagent or series of reagents that distinguishes between         methylated and non-methylated CpG dinucleotides within a         chromosomal region having a CD1D, and     -   2) detecting pancreatic cancer (e.g., afforded with a         sensitivity of greater than or equal to 80% and a specificity of         greater than or equal to 80%).         Preferably, the sensitivity is from about 70% to about 100%, or         from about 80% to about 90%, or from about 80% to about 85%.         Preferably, the specificity is from about 70% to about 100%, or         from about 80% to about 90%, or from about 80% to about 85%.

Genomic DNA may be isolated by any means, including the use of commercially available kits. Briefly, wherein the DNA of interest is encapsulated in by a cellular membrane the biological sample must be disrupted and lysed by enzymatic, chemical or mechanical means. The DNA solution may then be cleared of proteins and other contaminants, e.g., by digestion with proteinase K. The genomic DNA is then recovered from the solution. This may be carried out by means of a variety of methods including salting out, organic extraction, or binding of the DNA to a solid phase support. The choice of method will be affected by several factors including time, expense, and required quantity of DNA. All clinical sample types comprising neoplastic matter or pre-neoplastic matter are suitable for use in the present method, e.g., cell lines, histological slides, biopsies, paraffin-embedded tissue, body fluids, stool, colonic effluent, urine, blood plasma, blood serum, whole blood, isolated blood cells, cells isolated from the blood, and combinations thereof.

The technology is not limited in the methods used to prepare the samples and provide a nucleic acid for testing. For example, in some embodiments, a DNA is isolated from a stool sample or from blood or from a plasma sample using direct gene capture, e.g., as detailed in U.S. Pat. Appl. Ser. No. 61/485,386 or by a related method.

The genomic DNA sample is then treated with at least one reagent, or series of reagents, that distinguishes between methylated and non-methylated CpG dinucleotides within at least one marker comprising a DMR (e.g., DMR 1-107, e.g., as provided by Table 1) (e.g., DMR 1-449, e.g., as provided by Table 10).

In some embodiments, the reagent converts cytosine bases which are unmethylated at the 5′-position to uracil, thymine, or another base which is dissimilar to cytosine in terms of hybridization behavior. However in some embodiments, the reagent may be a methylation sensitive restriction enzyme.

In some embodiments, the genomic DNA sample is treated in such a manner that cytosine bases that are unmethylated at the 5′ position are converted to uracil, thymine, or another base that is dissimilar to cytosine in terms of hybridization behavior. In some embodiments, this treatment is carried out with bisulfate (hydrogen sulfite, disulfite) followed byt alkaline hydrolysis.

The treated nucleic acid is then analyzed to determine the methylation state of the target gene sequences (at least one gene, genomic sequence, or nucleotide from a marker comprising a DMR, e.g., at least one DMR chosen from DMR 1-107, e.g., as provided in Table 1) (at least one gene, genomic sequence, or nucleotide from a marker comprising a DMR, e.g., at least one DMR chosen from DMR 1-449, e.g., as provided in Table 10). The method of analysis may be selected from those known in the art, including those listed herein, e.g., QuARTS and MSP as described herein.

Aberrant methylation, more specifically hypermethylation of a marker comprising a DMR (e.g., DMR 1-107, e.g., as provided by Table 1) (e.g., DMR 1-449, e.g., as provided by Table 10) is associated with a cancer and, in some embodiments, predicts tumor site.

The technology relates to the analysis of any sample associated with a cancer of the gastrointestinal system. For example, in some embodiments the sample comprises a tissue and/or biological fluid obtained from a patient. In some embodiments, the sample comprises a secretion. In some embodiments, the sample comprises blood, serum, plasma, gastric secretions, pancreatic juice, a gastrointestinal biopsy sample, microdissected cells from a gastrointestinal biopsy, gastrointestinal cells sloughed into the gastrointestinal lumen, and/or gastrointestinal cells recovered from stool. In some embodiments, the subject is human. These samples may originate from the upper gastrointestinal tract, the lower gastrointestinal tract, or comprise cells, tissues, and/or secretions from both the upper gastrointestinal tract and the lower gastrointestinal tract. The sample may include cells, secretions, or tissues from the liver, bile ducts, pancreas, stomach, colon, rectum, esophagus, small intestine, appendix, duodenum, polyps, gall bladder, anus, and/or peritoneum. In some embodiments, the sample comprises cellular fluid, ascites, urine, feces, pancreatic fluid, fluid obtained during endoscopy, blood, mucus, or saliva. In some embodiments, the sample is a stool sample.

Such samples can be obtained by any number of means known in the art, such as will be apparent to the skilled person. For instance, urine and fecal samples are easily attainable, while blood, ascites, serum, or pancreatic fluid samples can be obtained parenterally by using a needle and syringe, for instance. Cell free or substantially cell free samples can be obtained by subjecting the sample to various techniques known to those of skill in the art which include, but are not limited to, centrifugation and filtration. Although it is generally preferred that no invasive techniques are used to obtain the sample, it still may be preferable to obtain samples such as tissue homogenates, tissue sections, and biopsy specimens

In some embodiments, the technology relates to a method for treating a patient (e.g., a patient with gastrointestinal cancer, with early stage gastrointestinal cancer, or who may develop gastrointestinal cancer), the method comprising determining the methylation state of one or more DMR as provided herein and administering a treatment to the patient based on the results of determining the methylation state. The treatment may be administration of a pharmaceutical compound, a vaccine, performing a surgery, imaging the patient, performing another test. Preferably, said use is in a method of clinical screening, a method of prognosis assessment, a method of monitoring the results of therapy, a method to identify patients most likely to respond to a particular therapeutic treatment, a method of imaging a patient or subject, and a method for drug screening and development.

In some embodiments of the technology, a method for diagnosing a gastrointestinal cancer in a subject is provided. The terms “diagnosing” and “diagnosis” as used herein refer to methods by which the skilled artisan can estimate and even determine whether or not a subject is suffering from a given disease or condition or may develop a given disease or condition in the future. The skilled artisan often makes a diagnosis on the basis of one or more diagnostic indicators, such as for example a biomarker (e.g., a DMR as disclosed herein), the methylation state of which is indicative of the presence, severity, or absence of the condition.

Along with diagnosis, clinical cancer prognosis relates to determining the aggressiveness of the cancer and the likelihood of tumor recurrence to plan the most effective therapy. If a more accurate prognosis can be made or even a potential risk for developing the cancer can be assessed, appropriate therapy, and in some instances less severe therapy for the patient can be chosen. Assessment (e.g., determining methylation state) of cancer biomarkers is useful to separate subjects with good prognosis and/or low risk of developing cancer who will need no therapy or limited therapy from those more likely to develop cancer or suffer a recurrence of cancer who might benefit from more intensive treatments.

As such, “making a diagnosis” or “diagnosing”, as used herein, is further inclusive of making determining a risk of developing cancer or determining a prognosis, which can provide for predicting a clinical outcome (with or without medical treatment), selecting an appropriate treatment (or whether treatment would be effective), or monitoring a current treatment and potentially changing the treatment, based on the measure of the diagnostic biomarkers (e.g., DMR) disclosed herein. Further, in some embodiments of the presently disclosed subject matter, multiple determination of the biomarkers over time can be made to facilitate diagnosis and/or prognosis. A temporal change in the biomarker can be used to predict a clinical outcome, monitor the progression of gastrointestinal cancer, and/or monitor the efficacy of appropriate therapies directed against the cancer. In such an embodiment for example, one might expect to see a change in the methylation state of one or more biomarkers (e.g., DMR) disclosed herein (and potentially one or more additional biomarker(s), if monitored) in a biological sample over time during the course of an effective therapy.

The presently disclosed subject matter further provides in some embodiments a method for determining whether to initiate or continue prophylaxis or treatment of a cancer in a subject. In some embodiments, the method comprises providing a series of biological samples over a time period from the subject; analyzing the series of biological samples to determine a methylation state of at least one biomarker disclosed herein in each of the biological samples; and comparing any measurable change in the methylation states of one or more of the biomarkers in each of the biological samples. Any changes in the methylation states of biomarkers over the time period can be used to predict risk of developing cancer, predict clinical outcome, determine whether to initiate or continue the prophylaxis or therapy of the cancer, and whether a current therapy is effectively treating the cancer. For example, a first time point can be selected prior to initiation of a treatment and a second time point can be selected at some time after initiation of the treatment. Methylation states can be measured in each of the samples taken from different time points and qualitative and/or quantitative differences noted. A change in the methylation states of the biomarker levels from the different samples can be correlated with gastrointestinal cancer risk, prognosis, determining treatment efficacy, and/or progression of the cancer in the subject.

In preferred embodiments, the methods and compositions of the invention are for treatment or diagnosis of disease at an early stage, for example, before symptoms of the disease appear. In some embodiments, the methods and compositions of the invention are for treatment or diagnosis of disease at a clinical stage.

As noted, in some embodiments, multiple determinations of one or more diagnostic or prognostic biomarkers can be made, and a temporal change in the marker can be used to determine a diagnosis or prognosis. For example, a diagnostic marker can be determined at an initial time, and again at a second time. In such embodiments, an increase in the marker from the initial time to the second time can be diagnostic of a particular type or severity of cancer, or a given prognosis. Likewise, a decrease in the marker from the initial time to the second time can be indicative of a particular type or severity of cancer, or a given prognosis. Furthermore, the degree of change of one or more markers can be related to the severity of the cancer and future adverse events. The skilled artisan will understand that, while in certain embodiments comparative measurements can be made of the same biomarker at multiple time points, one can also measure a given biomarker at one time point, and a second biomarker at a second time point, and a comparison of these markers can provide diagnostic information.

As used herein, the phrase “determining the prognosis” refers to methods by which the skilled artisan can predict the course or outcome of a condition in a subject. The term “prognosis” does not refer to the ability to predict the course or outcome of a condition with 100% accuracy, or even that a given course or outcome is predictably more or less likely to occur based on the methylation state of a biomarker (e.g., a DMR). Instead, the skilled artisan will understand that the term “prognosis” refers to an increased probability that a certain course or outcome will occur; that is, that a course or outcome is more likely to occur in a subject exhibiting a given condition, when compared to those individuals not exhibiting the condition. For example, in individuals not exhibiting the condition (e.g., having a normal methylation state of one or more DMR), the chance of a given outcome (e.g., suffering from a gastrointestinal cancer) may be very low.

In some embodiments, a statistical analysis associates a prognostic indicator with a predisposition to an adverse outcome. For example, in some embodiments, a methylation state different from that in a normal control sample obtained from a patient who does not have a cancer can signal that a subject is more likely to suffer from a cancer than subjects with a level that is more similar to the methylation state in the control sample, as determined by a level of statistical significance. Additionally, a change in methylation state from a baseline (e.g., “normal”) level can be reflective of subject prognosis, and the degree of change in methylation state can be related to the severity of adverse events. Statistical significance is often determined by comparing two or more populations and determining a confidence interval and/or a p value. See, e.g., Dowdy and Wearden, Statistics for Research, John Wiley & Sons, New York, 1983, incorporated herein by reference in its entirety. Exemplary confidence intervals of the present subject matter are 90%, 95%, 97.5%, 98%, 99%, 99.5%, 99.9% and 99.99%, while exemplary p values are 0.1, 0.05, 0.025, 0.02, 0.01, 0.005, 0.001, and 0.0001.

In other embodiments, a threshold degree of change in the methylation state of a prognostic or diagnostic biomarker disclosed herein (e.g., a DMR) can be established, and the degree of change in the methylation state of the biamarker in a biological sample is simply compared to the threshold degree of change in the methylation state. A preferred threshold change in the methylation state for biomarkers provided herein is about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 50%, about 75%, about 100%, and about 150%. In yet other embodiments, a “nomogram” can be established, by which a methylation state of a prognostic or diagnostic indicator (biomarker or combination of biomarkers) is directly related to an associated disposition towards a given outcome. The skilled artisan is acquainted with the use of such nomograms to relate two numeric values with the understanding that the uncertainty in this measurement is the same as the uncertainty in the marker concentration because individual sample measurements are referenced, not population averages.

In some embodiments, a control sample is analyzed concurrently with the biological sample, such that the results obtained from the biological sample can be compared to the results obtained from the control sample. Additionally, it is contemplated that standard curves can be provided, with which assay results for the biological sample may be compared. Such standard curves present methylation states of a biomarker as a function of assay units, e.g., fluorescent signal intensity, if a fluorescent label is used. Using samples taken from multiple donors, standard curves can be provided for control methylation states of the one or more biomarkers in normal tissue, as well as for “at-risk” levels of the one or more biomarkers in tissue taken from donors with metaplasia or from donors with a gastrointestinal cancer. In certain embodiments of the method, a subject is identified as having metaplasia upon identifying an aberrant methylation state of one or more DMR provided herein in a biological sample obtained from the subject. In other embodiments of the method, the detection of an aberrant methylation state of one or more of such biomarkers in a biological sample obtained from the subject results in the subject being identified as having cancer.

The analysis of markers can be carried out separately or simultaneously with additional markers within one test sample. For example, several markers can be combined into one test for efficient processing of a multiple of samples and for potentially providing greater diagnostic and/or prognostic accuracy. In addition, one skilled in the art would recognize the value of testing multiple samples (for example, at successive time points) from the same subject. Such testing of serial samples can allow the identification of changes in marker methylation states over time. Changes in methylation state, as well as the absence of change in methylation state, can provide useful information about the disease status that includes, but is not limited to, identifying the approximate time from onset of the event, the presence and amount of salvageable tissue, the appropriateness of drug therapies, the effectiveness of various therapies, and identification of the subject's outcome, including risk of future events.

The analysis of biomarkers can be carried out in a variety of physical formats. For example, the use of microtiter plates or automation can be used to facilitate the processing of large numbers of test samples. Alternatively, single sample formats could be developed to facilitate immediate treatment and diagnosis in a timely fashion, for example, in ambulatory transport or emergency room settings.

In some embodiments, the subject is diagnosed as having a gastrointestinal cancer if, when compared to a control methylation state, there is a measurable difference in the methylation state of at least one biomarker in the sample. Conversely, when no change in methylation state is identified in the biological sample, the subject can be identified as not having gastrointestinal cancer, not being at risk for the cancer, or as having a low risk of the cancer. In this regard, subjects having the cancer or risk thereof can be differentiated from subjects having low to substantially no cancer or risk thereof. Those subjects having a risk of developing a gastrointestinal cancer can be placed on a more intensive and/or regular screening schedule, including endoscopic surveillance. On the other hand, those subjects having low to substantially no risk may avoid being subjected to an endoscopy, until such time as a future screening, for example, a screening conducted in accordance with the present technology, indicates that a risk of gastrointestinal cancer has appeared in those subjects.

As mentioned above, depending on the embodiment of the method of the present technology, detecting a change in methylation state of the one or more biomarkers can be a qualitative determination or it can be a quantitative determination. As such, the step of diagnosing a subject as having, or at risk of developing, a gastrointestinal cancer indicates that certain threshold measurements are made, e.g., the methylation state of the one or more biomarkers in the biological sample varies from a predetermined control methylation state. In some embodiments of the method, the control methylation state is any detectable methylation state of the biomarker. In other embodiments of the method where a control sample is tested concurrently with the biological sample, the predetermined methylation state is the methylation state in the control sample. In other embodiments of the method, the predetermined methylation state is based upon and/or identified by a standard curve. In other embodiments of the method, the predetermined methylation state is a specifically state or range of state. As such, the predetermined methylation state can be chosen, within acceptable limits that will be apparent to those skilled in the art, based in part on the embodiment of the method being practiced and the desired specificity, etc.

Further with respect to diagnostic methods, a preferred subject is a vertebrate subject. A preferred vertebrate is warm-blooded; a preferred warm-blooded vertebrate is a mammal. A preferred mammal is most preferably a human. As used herein, the term “subject’ includes both human and animal subjects. Thus, veterinary therapeutic uses are provided herein. As such, the present technology provides for the diagnosis of mammals such as humans, as well as those mammals of importance due to being endangered, such as Siberian tigers; of economic importance, such as animals raised on farms for consumption by humans; and/or animals of social importance to humans, such as animals kept as pets or in zoos. Examples of such animals include but are not limited to: carnivores such as cats and dogs; swine, including pigs, hogs, and wild boars; ruminants and/or ungulates such as cattle, oxen, sheep, giraffes, deer, goats, bison, and camels; and horses. Thus, also provided is the diagnosis and treatment of livestock, including, but not limited to, domesticated swine, ruminants, ungulates, horses (including race horses), and the like. The presently-disclosed subject matter further includes a system for diagnosing a gastrointestinal cancer in a subject. The system can be provided, for example, as a commercial kit that can be used to screen for a risk of gastrointestinal cancer or diagnose a gastrointestinal cancer in a subject from whom a biological sample has been collected. An exemplary system provided in accordance with the present technology includes assessing the methylation state of a DMR as provided in Table 1.

EXAMPLES Example 1—Identifying Markers Using RRBS

Collectively, gastrointestinal cancers account for more deaths than those from any other organ system, and the aggregate incidence of upper gastrointestinal cancer and that of colorectal cancer (CRC) are comparable. To maximize the efficiency of screening and diagnosis, molecular markers for gastrointestinal cancer are needed that are site-specific when assayed from distant media such as blood or stool. While broadly informative, aberrantly methylated nucleic acid markers are often common to upper gastrointestinal cancers and CRC.

During the development of the technology provided herein, data were collected from a case-control study to demonstrate that a genome-wide search strategy identifies novel and informative candidate markers. Preliminary experiments demonstrated that stool assay of a methylated gene marker (BMP3) detects PanC. Then, it was shown that a combined assay of methylated BMP3 and mutant KRAS increased detection over either marker alone. However, markers discriminant in tissue proved to be poor markers in stool due to a high background of methylation, e.g., as detected in control specimens.

Study Population, Specimen Acquisition, and Samples

The target population was patients with pancreas cancer seen at the Mayo Clinic. The accessible population includes those who have undergone a distal pancreatectomy, a pancreaticoduodenectomy, or a colectomy with an archived resection specimen and a confirmed pathologic diagnosis. Colonic epithelial DNA was previously extracted from micro-dissected specimens by the Biospecimens Accessioning Processing (BAP) lab using a phenol-chloroform protocol. Data on the matching variables for these samples were used by Pancreas SPORE personnel to select tissue registry samples. These were reviewed by an expert pathologist to confirm case and control status and exclude case neoplasms arising from IPMN, which may have different underlying biology. SPORE personnel arranged for BAP lab microdissection and DNA extraction of the pancreatic case and control samples and provided 500 ng of DNA to lab personnel who were blinded to case and control status. Archival nucleic acid samples included 18 pancreatic adenocarcinomas, 18 normal pancreas, and 18 normal colonic epithelia matched on sex, age, and smoking status.

The sample types were:

-   -   1) Mayo Clinic Pancreas SPORE registry PanC tissues limited to         AJCC stage I and II;     -   2) control pancreata free from PanC;     -   3) archived control colonic epithelium free from PanC; and     -   4) colonic neoplasm from which DNA had been extracted and stored         in the BAP lab.         Cases and controls were matched by sex, age (in 5-year         increments), and smoking status (current or former vs. never).

Main Variables

The main variable was the methylation percentage of each individual 101 base-pair amplicon from HCP regions. The methylation percentage in case samples was compared to control samples following RRBS.

Methods

Libraries were prepared according to previously reported methods (see, e.g., Gu et al (2011) “Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling” Nature Protocols 6: 468-81) by fragmenting genomic DNA (300 ng) by digestion with 10 units of MspI, a methylation-specific restriction enzyme that recognizes CpG containing motifs. This treatment enriches the samples for CpG content and eliminates redundant areas of the genome. Digested fragments were end-repaired and A-tailed with 5 units of Klenow fragment (3′-5′ exo) and ligated overnight to Illumina adapters containing one of four barcode sequences to link each fragment to its sample ID. Size selection of 160-340 bp fragments (having 40-220 bp inserts) was performed using SPRI beads/buffer (AMPure XP, Beckman Coulter). Buffer cutoffs were from 0.7× to 1.1× of the sample volume of beads/buffer. Samples were eluted in a volume of 22 μl (EB buffer, Qiagen). qPCR was used to gauge ligation efficiency and fragment quality on a small aliquot of sample. Samples then underwent two rounds of bisulfite conversion using a modified EpiTect protocol (Qiagen). qPCR and conventional PCR (Pfu Turbo Cx hotstart, Agilent), followed by Bioanalyzer 2100 (Agilent) assessment on converted sample aliquots, determined the optimal PCR cycle number prior to amplification of the final library. The final PCR was performed in a volume of 50 μl (5 μl of 10×PCR buffer; 1.25 μl of each dNTP at 10 mM; 5 μl of a primer cocktail at approximately 5 μM, 15 μl of template (sample), 1 μl PfuTurbo Cx hotstart, and 22.75 μl water. Thermal cycling began with initial incubations at 95° C. for 5 minutes and at 98° C. for 30 seconds followed by 16 cycles of 98° C. for 10 seconds, 65° C. for 30 seconds, and at 72° C. for 30 seconds. After cycling, the samples were incubated at 72° C. for 5 minutes and kept at 4° C. until further workup and analysis. Samples were combined in equimolar amounts into 4-plex libraries based on a randomization scheme and tested with the bioanalyzer for final size verification. Samples were also tested with qPCR using phiX standards and adaptor-specific primers.

For sequencing, samples were loaded onto flow cell lanes according to a randomized lane assignment with additional lanes reserved for internal assay controls. Sequencing was performed by the NGS Core at Mayo's Medical Genome Facility on the Illumina HiSeq 2000. Reads were unidirectional for 101 cycles. Each flow cell lane generated 100-120 million reads, sufficient for a median coverage of 30× to 50× sequencing depth (based on read number per CpG) for aligned sequences. Standard Illumina pipeline software was used to analyze the reads in combination with RRBSMAP (Xi, et al. (2012) “RRBSMAP: a fast, accurate and user-friendly alignment tool for reduced representation bisulfite sequencing” Bioinformatics 28: 430-432) and an in-house pipeline (SAAP-RRBS) developed by Mayo Biomedical and Statistics personnel (Sun et al. (2012) “SAAP-RRBS: streamlined analysis and annotation pipeline for reduced representation bisulfite sequencing” Bioinformatics 28: 2180-1). The bioinformatic analyses consisted of 1) sequence read assessment and clean-up, 2) alignment to reference genome, 3) methylation status extraction, and 4) CpG reporting and annotation.

Statistical Considerations

The primary comparison evaluated methylation differences between cases and pancreatic controls at each CpG and/or tiled CpG window. The secondary comparison evaluated methylation differences between between cases and colon controls. Markers were tested for differential methylation by:

-   -   1. Assessing the distributions of methylation percentage for         each marker and discarding markers that were more than 1%         methylated in 10% of controls;     -   2. Testing the methylation distribution of the remaining markers         between cases and controls using the Wilcoxon rank sum test and         ranking markers by p-values; and     -   3. Using Q-values to estimate false discovery rates (FDR)         (Benjamini et al. (1995) “Multiple Testing” Journal of the Royal         Statistical Society. Series B (Methodological) 57: 289-300;         Storey et al. (2003) “Statistical significance for genomewide         studies” Proc Natl Acad Sci USA 100: 9440-5). At the         discovery-level, an FDR up to 25% is acceptable.

Analysis of Data

A data analysis pipeline was developed in the R statistical analysis software package (“R: A Language and Environment for Statistical Computing” (2012), R Foundation for Statistical Computing). The workflow comprised the following steps:

-   1. Read in all 6,101,049 CpG sites -   2. Identify for further analysis only those CpG sites where the     total group depth of coverage is 200 reads or more. This cut-off was     based on a power assessment to detect a difference of between 20%     and 30% methylation between any two groups because anything less     than this range has little chance of significance. Group depth of     coverage measures the number of reads for all subjects in a group     (e.g., if there are 18 subjects per group and each subject as 12     reads then the group depth of coverage is 12×18=216). -   3. Estimate the association of disease subtype with the methylation     % using variance inflated Poisson regression; the most discriminate     CpG sites were determined by comparing the model-fit X² to the 95th     percentile of all fitted models. Exclude all CpG sites where the     variance of the methylation percent across the groups is 0 because     these sites are non-informative CpG sites.     Applying the filters of 2 and 3 left a total of 1,217,523 CpG sites. -   4. Perform logistic regression on the % methylation (based on the     actual counts) using groups defined as Normal Colon, Normal     Pancreas, and Cancerous Pancreas. Since the variability in the %     methylation between subjects is larger than allowed by the binomial     assumption, an over-dispersed logistic regression model was used to     account for the increased variance. This dispersion parameter was     estimated using the Pearson Chi-square of the fit. -   5. From these model fits, calculate an overall F-statistic for the     group comparison based on the change in deviance between the models     with and without each group as a regressor. This deviance was scaled     by the estimated dispersion parameter. -   6. Create CpG islands on each chromosome based on the distance     between CpG site locations. Roughly, when the distance between two     CpG locations exceeds 100 bp, each location is defined as an     independent island. Some islands were singletons and were excluded. -   7. From the island definition above, the average F statistic is     calculated. When the F statistic exceeds 95% (i.e., top 5%) of all     CpG sites for the particular chromosome, a figure summary is     generated.     Further analysis comprised the following selection filters: -   1. ANOVA p-value cutoff <0.01 -   2. Ratios of % methylation PanC to normal pancreas and normal colon     >10 -   3. % methylation of normals <2% -   4. Number of contiguous CpGs meeting criteria ≥3     The methylation window was assessed to include additional contiguous     CpGs that exhibit significant methylation. Then, the candidates were     sorted by gene name for annotated regions and by chromosomal     location for nonannotated regions.

Results

Roughly 6 million CpGs were mapped at ≥10× coverage. More than 500 CpG islands met significance criteria for differential methylation. After applying the filter criteria above, 107 differentially methylated regions (DMR) were identified (Table 1).

TABLE 1 DMR DMR region on chromosome No. gene annotation chromosome strand (starting base-ending base) 1 none 1 F 35394805-35394875 2 none 1 F 240161479-240161546 3 none 1 R 156406057-156406118 4 AK055957 12 F 133484978-133485738 5 none 12 R 133484979-133485739 6 APBA2 15 F 29131299-29131369 7 none 2 F 71503632-71503860 8 PCBP3 21 R 47063793-47064177 9 TMEM200A 6 F 130687223-130687729 10 none 9 R 120507311-120507354 11 ABCB1 7 R 87229775-87229856 12 ADAMTS17 15 R 100881373-100881437 13 ADAMTS18 16 R 77468655-77468742 14 ADCY1 7 F 45613877-45614564 15 ADCY1 7 R 45613878-45614572 16 AGFG2 7 F 100136884-100137350 17 ARHGEF7 13 F 111767862-111768355 18 AUTS2 7 R 69062531-69062585 19 BTBD11 12 F 107715014-107715095 20 BVES 6 R 105584524-105584800 21 c13orf18 13 F 46960770-46961464 22 c13orf18 13 R 46960910-46961569 23 CACNA1C 12 F 2800665-2800898 24 CBLN1 16 R 49315846-49315932 25 CBS 21 F 44496031-44496378 26 CBS 21 R 44495926-44496485 27 CD1D 1 F 158150797-158151142 28 CELF2 10 F 11059508-11060151 29 CLEC11A 19 F 51228217-51228703 30 CLEC11A 19 R 51228325-51228732 31 CNR1 6 F 88876367-88876445 32 CNR1 6 R 88875699-88875763 33 CHRH2 7 F 30721941-30722084 34 DBNL 7 F 44084171-44084235 35 DBX1 11 R 20178177-20178304 36 DHRS12 13 F 52378159-52378202 37 DLL1 6 F 170598241-170600366 38 ELMO1 7 F 37487539-37488498 39 ELMO1 7 R 37487540-37488477 40 EN1 2 R 119607676-119607765 41 EOMES 3 F 27763358-27763617 42 FBLN1 22 R 45898798-45898888 43 FEM1C 5 F 114880375-114880442 44 FER1L4 20 R 34189679-34189687 45 FKBP2 11 F 64008415-64008495 46 FLT3 13 F 28674451-28674629 47 FNIP1 5 F 131132146-131132232 48 FOXP2 7 R 113727624-113727693 49 GFRA4 20 R 3641457-3641537 50 GJC1 17 F 42907705-42907798 51 GJC1 17 R 42907752-42907827 52 GRIN2D 19 F 48946755-48946912 53 HECW1 7 R 43152309-43152375 54 HOXA1 7 R 27136030-27136245 55 IFIH1 2 R 163174541-163174659 56 IGF2BP1 17 F 47073394-47073451 57 IKZF1 7 R 50343848-50343927 58 INSM1 (region 1) 20 F 20345123-20345150 59 INSM1 (region 2) 20 F 20350520-20350532 60 KCNK12 2 F 47797332-47797371 61 KCNN2 5 F 113696984-113697057 62 KCTD15 19 R 34287890-34287972 63 LINGO3 19 F 2290471-2290541 64 LOC100126784 11 R 19733958-19734013 65 LOC63930 20 F 61637950-61638000 66 LOC642345 13 R 88323571-88323647 67 MLLT1 19 R 6236992-6237089 68 MPND 19 R 4343896-4242968 69 MYEF2 15 F 48470117-48470606 70 NDUFAB1 16 R 23607524-23607650 71 NFASC 1 F 204797781-204797859 72 NR5A1 9 F 127266951-127267032 73 PDE6B 4 F 657586-657665 74 PLAGL1 6 R 144384503-144385539 75 PRKCB 16 R 23846964-23848004 76 PRRT3 3 F 9988302-9988499 77 PTF1A 10 F 23480864-23480913 78 RASGRF2 5 R 80256215-80256313 79 RIMKLA 1 R 42846119-42846174 80 RNF216 7 F 5821188-5821283 81 RSPO3 6 F 127440526-127441039 82 RSPO3 6 R 127440492-127440609 83 RYBP 3 R 72496092-72496361 84 SCARF2 22 F 20785373-20785464 85 SHH 7 F 155597771-155597951 86 SLC35E3 12 F 69140018-69140202 87 SLC38A3 3 R 50243467-50243553 88 SLC6A3 5 R 1445384-1445473 89 SPSB4 3 F 140770135-140770193 90 SRCIN1 17 R 36762706-36762763 91 ST6GAL2 2 F 107502978-107503055 92 ST6GAL2 2 R 107503155-107503391 93 ST8SIA1 12 F 22487528-22487827 94 ST8SIA1 12 R 22487664-22487848 95 ST8SIA6 10 F 17496177-17496310 96 SUSD5 3 R 33260338-33260423 97 TOX2 20 F 42544666-42544874 98 TWIST1 7 F 19156788-19157093 99 TWIST1 7 R 19156815-19157227 100 USP3 15 R 63795435-63795636 101 USP44 12 R 95942179-95942558 102 VIM 10 F 17271896-17271994 103 VWC2 7 R 49813182-49814168 104 WT1 11 R 32460759-32460800 105 ZFP30 19 F 38146299-38146397 106 ZNF570 19 F 37958078-37958134 107 ZNF71 19 F 57106617-57106852

In Table 1, bases are numbered according to the February 2009 human genome assembly GRCh37/hg19 (see, e.g., Rosenbloom et al. (2012) “ENCODE whole-genome data in the UCSC Genome Browser: update 2012” Nucleic Acids Research 40: D912-D917). The marker names BHLHE23 and LOC63930 refer to the same marker.

In these candidates, methylation signatures range from 3 neighboring CpGs to 52 CpGs. Some markers exhibit methylation on both strands; others are hemi-methylated. Since strands are not complimentary after bisulfite conversion, forward and reverse regions were counted separately. While Table 1 indicates the strand on which the marker is found, the technology is not limited to detecting methylation on only the indicated strand. The technology encompasses measuring methylation on either forward or reverse strands and/or on both forward and reverse strands; and/or detecting a change in methylation state on either forward or reverse strands and/or on both forward and reverse strands.

Methylation levels of the pancreatic cancers rarely exceeded 25% at filtered CpGs, which suggested that the cancer tissues may have high levels of contaminating normal cells and/or stroma. To test this, each of the cancers was sequenced for KRAS mutations to verify allele frequencies for the positive samples. For the 50% that harbored a heterozygous KRAS base change, the frequency of the mutant allele was at least 4 times less than the corresponding wild-type allele, in support of contamination by normal cells and/or stroma.

It was found that 7 of the 107 markers are in nonannotated regions and lie in genomic regions without protein coding elements. One marker is adjacent to a ncRNA regulatory sequence (AK055957). Of the remaining 99 candidate markers, approximately 30 have been described as associated with cancer, some of which classify as tumor suppressors. A few examples:

ADCY1 Down-regulated in osteosarcoma ELMO1 Promotes glioma invasion HOXA2 Hyper-methylated in cholangioca RSPO3 Wnt signalling regulator SUSD5 Mediates bone metastasis in lung cancer KCNK12 Hypermethylated in colon cancer CLEC11A Stem cell GF in leukemia USP3 Required for S-phase progression

The 69 other candidate markers have a previously identified weak association with cancer (e.g., mutations and/or copy number alterations observed in genome-wide screens) or have no previously identified cancer associations.

Example 2—Validating Markers

To validate the DMRs as cancer markers, two PCR-based validation studies were performed on expanded sample sets. The first study used samples from patient groups similar to those used in Example 1 (e.g., PanC, normal pancreas, normal colon) and added samples comprising buffy coat-derived DNA from normal patients who had no history of any cancer. The second study used using a selection of pan-GI cancers.

For the first validation study, a combination of previously run RRBS samples and newer banked samples were tested to verify technical accuracy and to confirm biological reproducibility, respectively. Methylation specific PCR (MSP) primers were designed for each of the marker regions, excluding only complementary strands in cases of non-strand specific methylation. Computer software (Methprimer) aided semi-manual design of the MSP primers by experienced personnel; assays were tested and optimized by qPCR with SYBR Green dyes on dilutions of universally methylated and unmethylated genomic DNA controls. The MSP primer sequences, each of which include 2-4 CpGs, were designed to provide a quick means of assessing methylation in the samples and were biased to maximize amplification efficiency. Primer sequences and physical parameters are provided in Table 2a and Table 2b:

TABLE 2a MSP primers Length GC Content SEQ Name (nt) Sequence(5'→ 3') (%) Tm Ta ID NO: abcb1f 21 GAT TTT GTT CGT CGT TAG TGC 42.9 52.3 60.0 1 abcb1r 19 TCT CTA AAC CCG CGA ACG A 52.6 56.0 60.0 2 adamts17f 25 TTC GAA GTT TCG GGA TAG GAA GCG T 48.0 60.0 65.0 3 adamts17r 20 CCT ACC GAC CTT CGA ACG CG 65.0 60.3 65.0 4 adamts18f 21 GGC GGC GCG TAT TTT TTT CGC 57.1 60.6 60.0 5 adamts18r 23 CGC TAC GAT ATA AAC GAC GAC GA 47.8 56.4 60.0 6 adcy1f 19 GGT TCG GTT GTC GTA GCG C 63.2 59.0 65.0 7 adcy1r 20 CCG ACC GTA ATC CTC GAC GA 60.0 58.6 65.0 8 agfg2f 25 TTA GGT CGG GAA TCG TTA TTG TTT C 40.0 55.1 60.0 9 agfg2r 22 GTA AT AAC CCC GCG CTA AAC G 50.0 56.5 60.0 10 arhgef7f 24 TTC GTT TGT TTT TCG GGT CGT AGC 45.8 58.1 60.0 11 arhgef7r 24 ACC ACG TAA CGA TTT ACT CGA CGA 45.8 57.8 60.0 12 auts2f 23 CGT TTT CGG ATT TGA AGT CGT TC 43.5 54.8 65.0 13 auts2r 19 CGC CTC GTC TTC CAA CGA A 57.9 57.7 65.0 14 btbd11f 19 AGG GCG TTC GGT TTT AGT C 52.6 55.1 60.0 15 btbd1r 22 AAC CGA AAA CGA CAA AT CGA T 36.4 53.4 60.0 16 Bvesf 21 TTT GAG CGG CGG TCG TTG ATC 57.1 60.4 60.0 17 Bvesr 22 TCC CCG AT CTA AAC GCT ACG A 50.0 57.8 60.0 18 C13orf18f 25 TTT AGG GAA GTA AAG CGT CGT TTT C 40.0 55.6 60.0 19 C13orf18r 22 AAC GAC GTC TCG ATA CCT ACG A 50.0 57.1 60.0 20 cacna1cf 22 GGA GAG TAT TTC GGT TTT TCG C 45.5 54.2 65.0 21 cacna1cr 24 ACA AAC AAA ATC GAA AAA CAC CCG 37.5 55.2 65.0 22 cbln1f 23 GTT TTC GTT TCG GTC GAG GTT AC 47.8 56.2 60.0 23 cbln1r 25 GCC ATT AAC TCG ATA AAA AAC GCG A 40.0 56.3 60.0 24 Cbsf 25 GAT TTA ATC GTA GAT TCG GGT CGT C 44.0 55.2 65.0 25 Cbsr 22 CCG AAA CGA ACG AAC TCA AAC G 50.0 56.8 65.0 26 cd1df 17 GCG CGT AGC GGC GTT TC 70.6 60.7 60.0 27 cd1dr 19 CCC ATA TCG CCC GAC GTA A 57.9 56.9 60.0 28 celf2f 22 TCG TAT TTG GCG TTC GGT AGT C 50.0 57.0 70.0 29 celf2r 21 CGA AT CCA ACG CCG AAA CGA 52.4 58.4 70.0 30 chr1 156f 24 TTG TCG TTC GTC GAA TTC GAT TTC 41.7 55.8 65.0 31 chr1 156r 23 AAC CCG ACG CTA AAA AAC GAC GA 47.8 59.2 65.0 32 chr1 240f 25 TTG CGT TGG TTA CGT TTT TTT ACG C 40.0 57.3 60.0 33 chr1 240r 23 ACG CCG TAC GAA TAA CGA AAC GA 47.8 58.7 60.0 34 chr1 353f 21 CGT TTT TCG GGT CGG GTT CGC 61.9 61.5 60.0 35 chr1 353r 19 TCC GAC GCT CGA CTC CCG A 68.4 63.1 60.0 36 chr12 133f 22 TCG GCG TAT TTT TCG TAG ACG C 50.0 57.6 60.0 37 chr12 133r 24 CGC AT CTT AAA CGT ACG CTT CGA 45.8 57.7 60.0 38 chr15 291 24 GGT TTA TAA AGA GTT CGG TTT CGC 41.7 54.4 60.0 39 (apba2)f chr15 291 24 AAA ACG CTA AAC TAC CCG AT ACG 41.7 55.3 60.0 40 (apba2)r chr2 715f 19 TGG GCG GGT TTC GTC GTA C 63.2 60.2 65.0 41 chr2 715r 21 GTC CCG AAA CAT CGC AAA CGA 52.4 58.2 65.0 42 chr6 130 20 GCG TTT GGA TTT TGC GTT C 55.0 58.0 60.0 43 (TMEM200A)f chr6 130 20 AAA ATA CGC CGC TAC CGA TA 55.0 60.6 60.0 44 (TMEM200A)r chr9 120f 20 GTT TAG GGA GTC GCG GTT AC 55.0 55.4 60.0 45 chr9 120r 23 CAA ATC CTA CGA ACG AAC GAA CG 47.8 56.2 60.0 46 clec11af 22 AGT TTG GCG TAG TCG GTA GAT C 50.0 56.4 60.0 47 clec11ar 22 GCG CGC AAA TAC CGA ATA AAC G 50.0 57.5 60.0 48 cnr1f 22 TCG GTT TTT AGC GTT CGT TCG C 50.0 58.4 60.0 49 cnr1r 23 AAA CAA CGA AAC GCC AT CCC GA 47.8 59.9 60.0 50 crhr2f 25 TAG TTT TTG GGC GTT ATT TTC GGT C 40.0 56.1 60.0 51 crhr2r 21 GCA ACT CCG TAC ACT CGA CGA 57.1 59.0 60.0 52 Dbnlf 26 TTT TTC GTT TGT TTT TCG GTA TTC GC 34.6 55.5 60.0 53 Dbnlr 22 CGA ATC CTA ACG AAC TAT CCG A 45.5 53.9 60.0 54 dbx1f 25 TTC GGT GGA TTT TCG TAT TGA TTT C 36.0 54.0 60.0 55 dbx1r 24 AAA CGA AAC CGC GAA CTA AAA CGA 41.7 57.6 60.0 56 dhrs12f 22 TTA CGT GAT AGT TCG GGG TTT C 45.5 54.6 60.0 57 dhrs12r 21 ATA AAA CGA CGC GAC GAA ACG 47.6 56.2 60.0 58 elmo1f 24 TTT CGG GTT TTG CGT TTT ATT CGC 41.7 57.2 60.0 59 elmo1r 28 GAA AAA AAA AAA CGC TAA AAA TAC GAC G 28.6 53.3 60.0 60 Eomesf 21 TAG CGC GTA GTG GTC GTA GTC 57.1 58.4 60.0 61 Eomesr 18 CCT CCG CCG CTA CAA CCG 72.2 61.5 60.0 62 fbln1f 22 TCG TTG TTT TAG GAT CGC GTT C 45.5 55.6 60.0 63 fbln1r 22 GAC GAA CGA TAA ACG ACG ACG A 50.0 56.9 60.0 64 fem1cf 21 TTC GGT CGC GTT GTT CGT TAC 52.4 58.0 60.0 65 fem1cr 25 AAA CGA AAA ACA ACT CGA ATA ACG A 32.0 53.8 60.0 66 fer1l4f 18 AGT CGG GGT CGG AGT CGC 72.2 62.3 60.0 67 fer1l4r 23 ATA AT CCC TCC GAA ACC CAC GA 47.8 58.2 60.0 68 fkbp2f 21 TCG GAA GTG ACG TAG GGT AGC 57.1 58.3 60.0 69 fkbp2r 19 CAC ACG CCC GCT AAC ACG A 63.2 60.6 60.0 70 flt3f 21 GCG CGT TCG GGT TTA TAT TGC 52.4 57.2 65.0 71 flt3r 20 GAC CAA CTA CCG CTA CTC GA 55.0 56.1 65.0 72 fnip1f 20 AGG GGA GAA TTT CGC GGT TC 55.0 57.6 65.0 73 fnip1r 24 AAC TAA ATT AAA CCT CAA CCG CCG 41.7 55.9 65.0 74 gfra4f 20 TTA GGA GGC GAG GTT TGC GC 60.0 60.3 65.0 75 gfra4r 28 GAC GAA ACC GTA ACG AAA ATA AAA ACG A 35.7 56.4 65.0 76 gjc1r 24 CGA ACT ATC CGA AAA AAC GAC GAA 41.7 55.6 65.0 77 glc1f 22 GCG ACG CGA GCG TTA ATT TTT C 50.0 57.6 65.0 78 hecw1f 23 TTC GCG TAT ATA TTC GTC GAG TC 43.5 54.2 60.0 79 hecw1r 20 CAC GAC CAC TAT CAC GAC GA 55.0 56.5 60.0 80 hoxa1f 22 GTA CGT CGG TTT AGT TCG TAG C 50.0 55.3 60.0 81 hoxa1r 21 CCG AAA CGC GAT ATC AAC CGA 52.4 57.6 60.0 82 ifih1f 20 CGG GCG GTT AGA GGG TTG TC 65.0 60.4 60.0 83 ifih1r 26 CTC GAA AT TCG TAA AAA CCC TCC GA 42.3 57.4 60.0 84 igf2bp1f 29 CGA GTA GTT TTT TTT TTT ATC GTT TAG AC 27.6 52.1 65.0 85 igf2bp1r 24 CAA AAA ACG ACA CGT AAA CGA TCG 41.7 55.2 65.0 86 ikzf1f 24 GTT TCG TTT TGC GTT TTT TTG CGC 41.7 57.5 65.0 87 ikzflr 19 TCC CGA ATC GCT ACT CCG A 57.9 57.8 65.0 88 insm1 reg1f 17 GCG GTT AGG CGG GTT GC 70.6 60.2 60.0 89 insm1 reg1r 25 ATT ATA TCA ATC CCA AAA ACA CGC G 36.0 54.3 60.0 90 insm1 reg2f 22 TAT TTT TCG AT TCG AGT TCG C 36.4 51.7 60.0 91 insm1 reg2r 22 TCA CCC GAT AAA AAC GAA AAC G 40.9 53.8 60.0 92 kcnk12f 21 GCG TCG TTA GTA GTA CGA AGC 52.4 55.3 60.0 93 kcnk12r 21 GCA CCT CAA CGA AAA CAC CGA 52.4 58.2 60.0 94 kcnn2f 23 TCG AGG CGG TTA ATT TTA TTC GC 43.5 55.8 65.0 95 kcnn2r 23 GCT CTA ACC CAA ATA CGC TAC GA 47.8 56.6 65.0 96 kctd15f 22 TCG GTT TCG AGG TAA GTT TAG C 45.5 54.7 60.0 97 kctd15r 23 CAC TTC GAA ACA AAA TTA CGC GA 39.1 54.3 60.0 98 lingo3f 20 GGA AGC GGA CGT TTT CGT TC 55.0 56.8 65.0 99 lingo3r 22 ACC CAA AT CCG AAA ACG ACG A 45.5 57.3 65.0 100 LOC100126784 19 AGG TTG CGG GCG TGA TTT C 57.9 58.8 65.0 101 (NAV2)f LOC100126784 20 CCA AAA CCA CGC GAA CAC GA 55.0 58.8 65.0 102 (NAV2)r LOC63930 20 GTT CGG AGT GTC GTA GTC GC 60.0 57.7 70.0 103 (bhlhe23)f LOC63930 21 AT CTC GCC TAC GAA ACG ACG 52.4 57.2 70.0 104 (bhlhe23)r LOC642345f 22 GTT TAG GGA CGT TTT CGT TTT C 40.9 52.5 65.0 105 LOC642345r 20 AAC GAA CGC TCG ATA ACC GA 50.0 56.5 65.0 106 mllt1f 20 TTT GGG TCG GGT TAG GTC GC 60.0 59.9 60.0 107 mllt1r 25 GAA ACC AAA AAA ACG CTA ACT CGT A 36.0 54.4 60.0 108 Mpndf 20 CGT TGT TGG AGT TTG GCG TC 55.0 57.1 65.0 109 Mpndr 21 TAC CCG AAC CGC GAT AAA ACG 52.4 57.5 65.0 110 myef2f 25 GGT ATA GTT CGG TTT TTA GTC GTT C 40.0 53.6 65.0 111 myef2r 24 TCT TTT CCT CCG AAA ACC GAA ACG 45.8 57.8 65.0 112 NDUFAB1f 23 GGT TAC GGT TAG TAT TCG GAT TC 43.5 53.0 60.0 113 NDUFAB1r 20 ATA TCA ACC GCC TAC CCG CG 60.0 59.7 60.0 114 NFASCf 24 TTT TGT TTT AT GCG GCG GTT GGC 45.8 59.6 65.0 115 NFASCr 22 TAT CCG AAC TAT CCG CTA CCG A 50.0 56.9 65.0 116 pcbp3f 19 GGT CGC GTC GTT TTC GAT C 57.9 56.6 60.0 117 pcbp3r 17 GCC GCA AAC GCC GAC GA 70.6 62.4 60.0 118 PDE6Bf 21 AT CGG CGG TAG TAC GAG TAC 52.4 56.1 55.0 119 PDE6Br 26 AAA CCA AT CCG TAA CGA TAA TAA CG 34.6 53.9 55.0 120 PLAGL1f 26 GAG TTT TGT TTT CGA AT TAT TTC GC 30.8 52.4 65.0 121 PLAGL1r 18 CCC GAA TTA CCG ACG ACG 61.1 55.7 65.0 122 PRKCBf 21 AGG TTC GGG TTC GAC GAT TTC 52.4 57.3 70.0 123 PRKCBr 21 AAC TCT ACA ACG CCG AAA CCG 52.4 57.7 70.0 124 PRRT3f 23 TTA GTT CGT TTA GCG ATG GCG TC 47.8 57.4 60.0 125 PRRT3r 20 CCG AAA CTA TCC CGC AAC GA 55.0 57.5 60.0 126 PTF1Af 21 TTC GTC GTT TGG GTT ATC GGC 52.4 57.8 60.0 127 PTF1Ar 23 GCC CTA AAA CTA AAA CAA CCG CG 47.8 57.1 60.0 128 RASGRF2f 22 GGT TGT CGT TTT AGT TCG TCG C 50.0 56.6 60.0 129 RASGRF2r 19 GCG AAA ACG CCC GAA CCG A 63.2 61.4 60.0 130 RIMKLAf 22 TCG TTT GGG AGA CGT ATT CGT C 50.0 56.7 60.0 131 RIMKLAr 25 ACT CGA AAA ATT TCC GAA CTA ACG A 36.0 55.0 60.0 132 RNF216f 20 TCG GCG GTT TTC GTT ATC GC 55.0 58.4 60.0 133 RNF216r 21 CCA CGA AAC TCG CAA CTA CGA 52.4 57.4 60.0 134 rspo3f 25 CGT TTA TTT AGC GTA ATC GTT TCG C 40.0 55.0 65.0 135 rspo3r 24 GAA TAA CGA ACG TTC GAC TAC CGA 45.8 56.6 65.0 136 RYBPf 24 CGG ACG AGA TTA GTT TTC GTT AGC 45.8 55.7 60.0 137 RYBPr 24 TCG TCA ATC ACT CGA CGA AAA CGA 45.8 58.4 60.0 138 SCARF2f 22 TCG GTT CGT AGG TAT ACG TGT C 50.0 55.8 60.0 139 SCARF2r 22 GCT ACT ACC AAT ACT TCC GCG A 50.0 56.4 60.0 140 SLC35E3f 21 GTT AGA CGG TTT TAG TTT CGC 42.9 51.8 60.0 141 SLC35E3r 20 AAA AAC CCG ACG ACG ATT CG 50.0 55.8 60.0 142 slc38a3f 21 GTT AGA GTT CGC GTA GCG TAC 52.4 55.3 65.0 143 slc38a3r 25 GAA AAA ACC AAC CGA ACG AAA ACG A 40.0 56.9 65.0 144 slc6a3f 19 CGG GGC GTT TCG ATG TCG C 68.4 62.0 65.0 145 slc6a3r 24 CCG AAC GAC CAA ATA AAA CCA ACG 45.8 57.0 65.0 146 srcin1f 22 CGT TTT ATG TTG GGA GCG TTC G 50.0 56.8 65.0 147 srcin1r 20 GAC CGA ACC GCG TCT AAA CG 60.0 58.5 65.0 148 st6gal2f 21 TAC GTA TCG AGG TTG CGT CGC 57.1 59.3 65.0 149 st6gal21 25 AAA CTC TAA AAC GAA CGA AAC TCG A 36.0 54.9 65.0 150 st8sia1f 21 TCG AGA CGC GTT TTT TGC GTC 52.4 58.7 60.0 151 st8sia1r 20 AAC GAT CCC GAA CCG CCG TA 60.0 61.3 60.0 152 ST8SIA6f 21 CGA GTA GTG CGT TTT TCG GTC 52.4 56.2 60.0 153 ST8SIA6r 22 GAC AAC AAC GAT AAC GAC GAC G 50.0 56.1 60.0 154 SUSD5f 22 AGC GTG CGT TAT TCG GTT TTG C 50.0 59.1 65.0 155 SUSD5r 23 ACC TAC GAT TCG TAA ACC GAA CG 47.8 56.9 65.0 156 TOX2f 23 AGT TCG CGT TTT TTT CGG TCG TC 47.8 58.5 70.0 157 TOX2r 21 AAC CGA CGC ACC GAC TAA CGA 57.1 61.0 70.0 158 twist1f 22 TTG CGT CGT TTG CGT TTT TCG C 50.0 59.9 60.0 159 twist1r 20 CAA CTC GCC AAT CTC GCC GA 60.0 60.2 60.0 160 USP3f 18 TAT TGC GGG GAG GTG TTC 55.6 54.7 60.0 161 USP3r 24 TCA AAA AAT AAT TAA CCG AAC CGA 29.2 51.3 60.0 162 USP44f 24 TTA GTT TTC GAA GTT TTC GTT CGC 37.5 54.4 60.0 163 USP44r 19 TCC GAC CCT ATC CCG ACG A 63.2 59.9 60.0 164 VIMf 27 GAT TAG TTA ATT AAC GAT AAA GTT CGC 29.6 51.0 60.0 165 VIMr 23 CCG AAA ACG CAT AAT ATC CTC GA 43.5 55.0 60.0 166 vwc2f 26 TTG GAG AGT TTT TCG AAT TTT TTC GC 34.6 55.2 65.0 167 vwc2r 19 GAA AAC CAC CCT AAC GCC G 57.9 56.6 65.0 168 wt1f 17 CGC GGG GTT CGT AGG TC 70.6 58.5 65.0 169 wt1r 23 CGA CAA ACA ACA ACG AAA TCG AA 39.1 54.5 65.0 170 zfp30f 22 AGT AGC GGT TAT AGT GGC GTT C 50.0 56.7 65.0 171 zfp30r 22 GCA TTC GCG ACG AAA ACA AAC G 50.0 58.0 65.0 172 ZNF569f 20 GTA TTG AGG TCG GCG TTG TC 55.0 55.9 60.0 173 ZNF569r 19 CCG CCC GAA TAA ACC GCG A 63.2 60.8 60.0 174 ZNF71f 20 CGT AGT TCG GCG TAG TTC GC 60.0 58.2 65.0 175 ZNF71r 21 AAC CCG CCC GAC GAC AT ACG 61.9 62.1 65.0 176 In Table 2a, Ta is the optimized annealing temperature and Tm is the melting temperature in ° C. in 50 mM NaCl. Primers celf2f and celf2r; LOC63930 (bhlhe23)f and LOC63930 (bhlhe23)r; PRKCBf and PRKCBr; and TOX2f and TOX2r are used in a 2-step reaction.

Specimens

Archived DNA samples from Mayo clinic patients were used for both validations. Cases and controls were blinded and matched by age and sex. The first sample set included DNA from 38 pancreatic adenocarcinomas and controls (20 normal colonic epithelia, 15 normal pancreas, and 10 normal buffy coats). The second sample set included DNA from 38 colorectal neoplasms (20 colorectal adenocarcinomas and 18 adenomas >1 cm), 19 esophageal adenocarcinomas, 10 gastric (stomach) cancers, and 10 cholangiocarcinomas.

Methods

Archived DNA was re-purified using SPRI beads (AMPure XP-Beckman Coulter) and quantified by absorbance. 1-2 μg of sample DNA was then treated with sodium bisulfite and purified using the EpiTect protocol (Qiagen). Eluted material (10-20 ng) was amplified on a Roche 480 LightCycler using 384-well blocks. Each plate accommodated 4 markers (and standards and controls), thus using a total of 23 plates. The 88 MSP assays had differing optimal amplification profiles and were grouped accordingly. Specific annealing temperatures are provided in Table 2. The 20-μl reactions were run using LightCycler 480 SYBR I Master mix (Roche) and 0.5 μmol of primer for 50 cycles and analyzed, generally, by the 2nd-derivative method included with the LightCycler software. The raw data, expressed in genomic copy number, was normalized to the amount of input DNA, and tabulated. Analysis at the tissue level comprised performing PCA (supplemented with k-fold cross validation), elastic net regression, and constructing box plots of non-zero elastic net markers. In this way, markers were collectively ranked. Of these candidates, because of the importance of minimizing normal cellular background methylation for stool and blood-based assays, the ranking was weighed toward those markers which exhibited the highest fold-change differential between cases and controls.

Results

Among the 107 methylated DNA markers with proven discrimination for GI cancers, MSP validation was performed on 88 from which subsets were identified for display of more detailed summary data.

Detection of Pancreatic Cancer

A subset of the methylation markers were particularly discriminant for pancreatic cancer: ABCB1, ADCY1, BHLHE23 (LOC63930), c13orf18, CACNA1C, chr12 133, CLEC11A, ELMO1, EOMES, GJC1, IHIF1, IKZF1, KCNK12, KCNN2, PCBP3, PRKCB, RSPO3, SCARF2, SLC38A3, ST8SIA1, TWIST1, VWC2, WT1, and ZNF71 (see Table 1). Individual AUC values (PanC versus normal pancreas or colon) for these markers were above 0.87, which indicates superior clinical sensitivity and specificity.

Initially, the two best stand-alone markers appeared to be CLEC11A and c13orf18, which were 95% and 82% sensitive for pancreatic cancer, respectively, at 95% specificity. Additional experiments designed additional primers to target the most specific CpGs within specified DMRs of selected markers. These additional primers enhanced discrimination further. For example, design of new MSP for the marker PRKCB (initial sensitivity of 68%) dramatically increased discrimination for pancreatic cancer and achieved sensitivity of 100% at 100% specificity. Moreover, the median methylation signal-to-noise ratio for this marker, comparing cancer to normal tissue, was greater than 8000. This provides a metric critical to the detection of cancer markers in samples with high levels of normal cellular heterogeneity. Having base level methylation profiles of the DMRs from the filtered RRBS data allows for the construction of highly sensitive and specific detection assays. These results obtained from the improved MSP designs demonstrate that similar performance specifications can be obtained from the other 106 DMRs with additional design improvements, validation, and testing formats.

TABLE 2b MSP primers GC  SEQ  Length Content ID Name (nt) Sequence(5' → 3') (%) Tm Ta NO: dll(sense)r 20 GTC GAG CGC GTT CGT TGT AC 60.0 58.9 65 177 dll(sense)r 22 GAC CCG AAA AAT AAA TCC CGA A 40.9 53.3 65 178 dll(antisense)f 24 GAT TTT TTT AGT TTG TTC GAC GGC 37.5 53.5 65 179 dll(antisense)r 25 AAA ATT ACT AAA CGC GAA ATC GAC G 36.0 54.4 65 180 en1(sense)f 26 TAA TGG GAT GAT AAA TGT ATT CGC GG 38.5 55.2 65 181 en1(sense)r 26 ACC GCC TAA TCC AAC TCG AAC TCG TA 50.0 61.2 65 182 en1(antisense)f 22 GGT GTT TTT AAA GGG TCG TCG T 45.5 55.7 65 183 en1(antisense)r 19 GAC CCG ACT CCT CCA CGT A  63.2 58.4 65 184 foxp2(sense)f 30 GGA AGT TTA TAG TGG TTT CGG CGG GTA GGC 53.3 63.6 60 185 foxp2(sense)r 22 GCG AAA AAC GTT CGA ACC CGC G 59.1 61.9 60 186 grin2d(sense)f 28 TGT CGT CGT CGC GTT ATT TTA GTT GTT C 42.9 59.2 60 187 grin2d(sense)r 22 AAC CGC CGT CCA AAC CAT CGT A 54.6 61.3 60 188 nr5a1(sense)f 25 GAA GAG TTA GGG TTC GGG ACG CGA G 60.0 62.6 65 189 nr5a1(sense)r 25 AAC GAC CAA ATA AAC GCC GAA CCG A 48.0 61.1 65 190 nr5a1(antisense)f  25 CGT AGG AGC GAT TAG GTG GGC GTC G 64.0 64.6 60 191 nr5a1(antisense)r  23 AAA CCA AAA CCC GAA ACG CGA AA 43.5 58.5 60 192 shh(sense)f 26 CGA TTC GGG GGA TGG ATT AGC GTT GT 53.9 62.6 65 193 shh(sense)r 30 CGA AAT CCC CCT AAC GAA AAT CTC CGA AAA 43.3 60.4 65 194 shh(antisense)f 25 CGG GGT TTT TTT AGC GGG GGT TTT C 52.0 61.0 65 195 shh(antisense)r  29 CGC GAT CCG AAA AAT AAA TTA ACG CTA CT 37.9 57.8 65 196 spsb4(sense)f 20 AGC GGT TCG AGT TGG GAC GG 65.0 62.3 65 197 spsb4(sense)r 24 GAA AAA CGC GAT CGC CGA AAA CGC 54.2 61.8 65 198 spsb4(antisense)f 28 GAA GGT TAT TAA TTT AAT AGT CGC GGA A 32.1 53.7 65 199 spsb4(antisense)r 25 AAA AAA AAC GTT CCC GAC GAC CGC G  52.0 62.4 65 200 prkcbf(re-design) 25 AGT TGT TTT ATA TAT CGG CGT TCG G  40.0 55.3 65 201 prkcbr(re-design) 23 GAC TAT ACA CGC TTA ACC GCG AA  47.8 56.9 65 202 In Table 2b, Ta is the optimized annealing temperature and Tm is the melting temperature in ° C. in 50 mM NaCl.

Detection of Other GI Neoplasms

The markers were then assessed in the 2nd set of samples, which included other GI cancers and precancers as indicated above. The methods, including reaction conditions and platform, were identical to the first validation described above. Data were normalized to the amount of input DNA, allowing copy numbers to be compared between the two validations. Analysis consisted of PCA and k-fold cross-validation, as before.

Some methylation sequences that were identified exhibited extraordinary degrees of discrimination, even as stand-alone markers. For example, IKZF1 had 95% sensitivity for adenoma and 80% sensitivity for CRC, with virtually no background methylation in normal samples. The S/N ratios were in excess of 10,000—a degree of discrimination rarely seen with any class of markers. The chr12.133 assay, specific to a completely un-annotated and un-described stretch of methylated DNA, was also adept at detecting all cancers equally well. Several markers (cd1d, chr12.133, clec11a, elmo1, vwc2, zuf71) individually achieved perfect discrimination for gastric cancer, as did twist1 for colorectal cancer (Table 6).

Tumor Site Prediction

The data collected during the development of embodiments of the technology demonstrate that the methylation states of particular DNA markers accurately predict neoplasm site. In this analysis, a recursive partitioning regression model was used in a decision tree analysis based on combinations of markers with complementary performance to generate a robust site classification.

In particular, statistical analyses were performed to validate the sensitivity and specificity of marker combinations. For example, using a “Random Forest” model (see, e.g., Breiman (2001) “Random Forests” Machine Learning 45: 5-32), tree models were constructed using recursive partitioning tree regression, e.g., as implemented by the rPart package in the R statistical software. Recursive partitioning tree regression is a regression technique which tries to minimize a loss function and thus maximize information content for classification problems. The tree is built by the following process: first the single variable is found that best splits the data into two groups. The data is separated, and then this process is applied separately to each sub-group, and so on recursively until the subgroups either reach a minimum size or until no improvement can be made. The second stage of the procedure consists of using cross-validation to trim back the full tree. A cross validated estimate of risk is computed for a nested set of sub trees and a final model is produced from the sub tree with the lowest estimate of risk. See, e.g., Therneau (2012) “An Introduction to Recursive Partitioning Using RPART Routines”, available at The Comprehensive R Archive Network; Breiman et al. (1983) “Classification and Regression Trees” Wadsworth, Belmont, C A; Clark et al. (1992) “Tree-based models” in J. M. Chambers and T. J. Hastie, eds., Statistical Models in S, chapter 9. Wadsworth and Brooks/Cole, Pacific Grove, Calif.; Therneau (1983) “A short introduction to recursive partitioning” Orion Technical Report 21, Stanford University, Department of Statistics; Therneau et al. (1997) “An introduction to recursive partitioning using the rpart routines” Divsion of Biostatistics 61, Mayo Clinic.

As used in this analysis, the classification is Upper GI Lesion vs. Lower GI Lesion vs. Normal Samples. At each node of the regression, all variables are considered for entry but only the variable with the greatest decrease in risk of predicted outcome is entered. Subsequent nodes are added to the tree until there is no change in risk. To avoid overfitting, random forest regression was used. In this approach, 500 prediction trees were generated using bootstrapping of samples and random selection of variables. To determine the importance of the i-th variable, the i-th variable is set aside and the corresponding error rates for the full fit (including all data) vs. the reduced fit (all data except the i-th variable) using all 500 predictions are compared.

A forest of 500 trees was constructed to test the predictive power of candidate markers for discriminating among normal tissue, upper gastrointestinal lesions, and lower gastrointestinal lesions. This procedure is done at a very high level of robustness. First, for each tree creation, a bootstrap sample is taken of the dataset to create a training set and all observations not selected are used as a validation set. At each branch in the tree, a random subset of markers is used and evaluated to determine the best marker to use at that particular level of the tree. Consequently, all markers have an equal chance of being selected. The technique provides a rigorous validation and assessment of the relative importance of each marker. Each of the 500 trees is allowed to “vote” on which class a particular sample belongs to with the majority vote winning. The estimated misclassification rate is estimated from all samples not used for a particular tree.

To test the relative importance of a given marker, the validation set is again used. Here, once a tree is fit, the validation data is passed down the tree and the correct classification rate is noted. Then, the marker values are randomly permuted within the m-th marker, they are passed down the tree, and the correct classification is again noted. If a marker has high importance, the actual data provides a better classification than the randomly permuted data. Misclassification by the permuted data is referred to as the Mean Decrease in Accuracy. If a marker is not important, the actual data will provide a similar classification as the randomly permuted data. FIG. 1 is a plot of the marker importance as measured by Mean Decrease in Accuracy. The vertical lines are at 2.5% and 5%. These data indicate that, e.g., for clec11a the estimated Mean Decrease in Accuracy is approximately 12%, indicating that when randomly permuting the results of this marker, the overall accuracy of the prediction decreases by 12%. FIG. 1 lists the markers in order of importance.

The estimated overall misclassification rate of the 500 trees in the forest was 0.0989. The results of the voting process across all 500 trees in the forest is summarized in Table 3 and expanded by subtype in Table 4. In the tables, the tissue sample type is listed in the first column (e.g., non-cancerous (“Normal”), upper gastrointestinal cancer (“Upper”), or lower gastrointestinal cancer (“Lower”) in Table 3; adenoma (“Ad”), normal colon (“Colo Normal”), colorectal cancer (“CRC”), esophageal cancer (“Eso C”), pancreatic cancer (“Pan C”), normal pancreas (“Pan Normal”), and stomach cancer (“Stomach C”) in Table 4). A quantitative classification of the sample by the analysis is provided as a number is columns 1, 2, or 3, for classification as an upper gastrointestinal cancer (column 1), a lower gastrointestinal cancer (column 2), or a normal tissue (column 3), respectively. The numbers provide a measure indicating the success rate of the classifier (e.g., the number of times the classifier classified the sample type in the first column as the type indicated in the first row).

TABLE 3 1 2 3 class.error Upper 59.00 1.00 7.00 0.12 Lower 3.00 33.00 2.00 0.13 Normal 1.00 0.00 44.00 0.02 column 1 = upper GI; column 2 = lower GI; Column 3 = normal

TABLE 4 Predicted by Model Sample type UGIC* CRN** Normal UGIC* Pancreas Cancer 35 0 3 Esophagus Cancer 15 0 3 Stomach Cancer 9 1 0 CRN** Colon Cancer 2 16 2 Colon Adenoma 1 17 0 Controls Pancreas Normal 0 0 15 Colon Normal 0 0 20 Buffy Coat Normal 1 0 9 *UGIC = Upper GI Cancer, **CRN = CRC + Adenoma ≥ 1 cm

Additional analysis demonstrated that a combination of two markers accurately predicted tumor site in >90% of samples, the top 17 two-marker combinations accurately predicted tumor site in >80% of samples, and the top 49 combinations accurately predicted tumor site in 70% of the samples. This observation that multiple combinations of DNA methylation markers accurately predict tumor site demonstrates the robustness of the technology.

Using the top two markers in the recursive partition decision tree, all normal tissues were correctly classified as normal, all gastric cancers were correctly classified as upper GI, nearly all esophageal and pancreatic cancers were correctly classified as upper GI, and nearly all colorectal cancers and precancers (adenomas) were correctly classified as lower GI. During the development of embodiments of the technology provided herein, statistical analyses focused on a set of specific markers consisting of clec11a, c13orf18, kcnn2, abcb1, slc38a3, cd1c, ikzf1, adcy1, chr12133, rspo3, and twist1. In particular, statistical analyses described above were directed toward identifying sets of markers (e.g., having two or more markers) that provide increased power to identify cancer and/or discriminate between cancers. Table 5 summarizes the accuracy for each pairwise set of markers, namely clec11a, c13orf18, kcnn2, abcb1, slc38a3, cd1c, ikzf1, adcy1, chr12133, rspo3, and twist1. According to this analysis, the pair of markers consisting of clec11a and twist1 is the most informative, but various other combinations have similar accuracy.

TABLE 5 Accuracy for Site Prediction Using Various Marker Combinations accuracy markers 90.7 clec11a twist1 88.7 clec11a chr12.133 88.7 clec11a rspo3 88 clec11a ikzf1 86.7 clec11a adcy1 84.7 twist1 c13orf18 84 clec11a cd1d 83.3 twist1 abcb1 83.3 c13orf18 chr12.133 83.3 abcb1 chr12.133 83.3 abcb1 rspo3 82 c13orf18 rspo3 81.3 abcb1 ikzf1 80.7 abcb1 adcy1 80 twist1 kcnn2 80 c13orf18 adcy1 80 cd1d rspo3 79.3 c13orf18 cd1d 79.3 kcnn2 adcy1 79.3 kcnn2 rspo3 79.3 cd1d ikzf1 78.7 c13orf18 ikzf1 77.3 kcnn2 ikzf1 77.3 abcb1 cd1d 76.7 twist1 cd1d 76.7 kcnn2 chr12.133 76.7 chr12.133 rspo3 76 cd1d chr12.133 75.3 twist1 rspo3 75.3 kcnn2 cd1d 74.7 twist1 ikzf1 74 twist1 slc38a3 74 slc38a3 ikzf1 74 slc38a3 chr12.133 73.3 twist1 chr12.133 73.3 slc38a3 adcy1 73.3 adcy1 rspo3 72.7 slc38a3 rspo3 72 cd1d adcy1 72 ikzf1 chr12.133 72 adcy1 chr12.133 71.3 ikzf1 adcy1 70.7 clec11a c13orf18 70.7 clec11a kcnn2 70.7 clec11a abcb1 70.7 clec11a slc38a3 70.7 ikzf1 rspo3 70 twist1 adcy1 70 kcnn2 abcb1 68 slc38a3 cd1d 66.7 c13orf18 abcb1 65.3 c13orf18 kcnn2 65.3 kcnn2 slc38a3 64.7 c13orf18 slc38a3 56 abcb1 slc38a3

Example 3—AUC Analysis of Individual Markers

Statistical analysis included principle component analysis to identify uncorrelated linear combinations of the markers whose variance explains the greatest percentage of variability observed in the original data. The analysis determined the relative weights of each marker to discriminate between treatment groups. As a result of this analysis, end-point AUC values were determined for a subset of the markers that measure each marker's power to discriminate a specific cancer (esophageal, stomach, pancreatic, colorectal, and adenoma) from 1) the other cancer types and from 2) normal samples (e.g., not comprising cancer tissue or not from a patient having cancer or who may develop cancer). These data are provided in Table 6.

TABLE 6 AUC values for a subset of markers BMP3 NDRG4 abcb1 adcy1 Eso C. vs. Other 0.51 0.58 0.67 0.39 Eso C. vs. Normal 0.82 0.86 0.83 0.63 Stomach C. vs. Other 0.72 0.70 0.87 0.65 Stomach C. vs. Normal 0.91 0.95 1.00 0.86 Pan C. vs. Other 0.59 0.66 0.73 0.69 Pan C. vs. Normal 0.90 0.90 0.91 0.94 CRC. vs. Other 0.74 0.59 0.46 0.69 CRC. vs. Normal 0.91 0.87 0.72 0.86 Ad. vs. Other 0.74 0.71 0.35 0.71 Ad. vs. Normal 0.96 0.94 0.61 0.99 c13orf18 cacnale cd1d chr12.133 Eso C. vs. Other 0.60 0.27 0.52 0.52 Eso C. vs. Normal 0.75 0.42 0.85 0.86 Stomach C. vs. Other 0.78 0.70 0.75 0.81 Stomach C. vs. Normal 0.88 0.96 1.00 1.00 Pan C. vs. Other 0.81 0.85 0.73 0.57 Pan C. vs. Normal 0.89 0.96 0.94 0.86 CRC. vs. Other 0.37 0.56 0.67 0.73 CRC. vs. Normal 0.51 0.75 0.88 0.89 Ad. vs. Other 0.21 0.42 0.54 0.72 Ad. vs. Normal 0.35 0.53 0.88 0.99 clec11a ebmol eomes glc1 Eso C. vs. Other 0.55 0.46 0.37 0.51 Eso C. vs. Normal 0.81 0.76 0.54 0.69 Stomach C. vs. Other 0.84 0.76 0.70 0.74 Stomach C. vs. Normal 1.00 1.00 0.89 0.88 Pan C. vs. Other 0.89 0.62 0.70 0.54 Pan C. vs. Normal 0.98 0.93 0.87 0.73 CRC. vs. Other 0.31 0.71 0.61 0.64 CRC. vs. Normal 0.56 0.83 0.79 0.80 Ad. vs. Other 0.35 0.70 0.59 0.65 Ad. vs. Normal 0.59 0.92 0.77 0.82 ibif1 kcnk12 kcnn2 loc63930 Eso C. vs. Other 0.11 0.39 0.68 0.40 Eso C. vs. Normal 0.10 0.66 0.84 0.69 Stomach C. vs. Other 0.80 0.65 0.76 0.65 Stomach C. vs. Normal 0.98 0.90 0.91 0.88 Pan C. vs. Other 0.91 0.71 0.76 0.61 Pan C. vs. Normal 0.97 0.94 0.91 0.88 CRC. vs. Other 0.50 0.71 0.46 0.84 CRC. vs. Normal 0.58 0.93 0.67 0.95 Ad. vs. Other 0.21 0.67 0.30 0.69 Ad. vs. Normal 0.22 0.92 0.47 0.93 prkcb rspo3 scarf2 slc38a3 Eso C. vs. Other 0.44 0.42 0.13 0.34 Eso C. vs. Normal 0.62 0.68 0.21 0.50 Stomach C. vs. Other 0.71 0.64 0.70 0.81 Stomach C. vs. Normal 0.85 0.86 0.82 0.97 Pan C. vs. Other 0.74 0.57 0.93 0.83 Pan C. vs. Normal 0.90 0.93 0.94 0.96 CRC. vs. Other 0.56 0.80 0.49 0.57 CRC. vs. Normal 0.71 0.93 0.57 0.73 Ad. vs. Other 0.46 0.82 0.26 0.32 Ad. vs. Normal 0.66 1.00 0.34 0.47 twist1 vwe2 wt1 znf71 Eso C. vs. Other 0.42 0.52 0.35 0.70 Eso C. vs. Normal 0.74 0.83 0.66 0.90 Stomach C. vs. Other 0.58 0.78 0.70 0.89 Stomach C. vs. Normal 0.92 1.00 0.91 1.00 Pan C. vs. Other 0.67 0.58 0.76 0.50 Pan C. vs. Normal 0.94 0.92 0.98 0.79 CRC. vs. Other 0.83 0.72 0.60 0.63 CRC. vs. Normal 1.00 0.90 0.92 0.91 Ad. vs. Other 0.70 0.76 0.64 0.64 Ad. vs. Normal 0.95 0.98 0.89 0.90 st8sia1 ikzf1 pcbp3 PCA1 Eso C. vs. Other 0.45 0.55 0.54 0.47 Eso C. vs. Normal 0.64 0.88 0.86 0.79 Stomach C. vs. Other 0.77 0.74 0.76 0.81 Stomach C. vs. Normal 0.92 0.97 0.97 0.99 Pan C. vs. Other 0.65 0.49 0.64 0.72 Pan C. vs. Normal 0.93 0.85 0.86 0.96 CRC. vs. Other 0.58 0.81 0.67 0.68 CRC. vs. Normal 0.74 0.94 0.90 0.96 Ad. vs. Other 0.67 0.80 0.63 0.62 Ad. vs. Normal 0.84 0.99 0.86 0.98

Example 4—Barrett's Esophagus and Esophageal Cancer

Development of esophageal cancer is closely linked with Barrett's epithelial metaplasia and pancreatic adenocarcinoma arises from discrete mucous cell metaplasias. See, e.g., Biankin et al (2003) “Molecular pathogenesis of precursor lesions of pancreatic ductal adenocarcinoma” Pathology 35:14-24; Cameron et al (1995) “Adenocarcinoma of the esophagogastric junction and Barrett's esophagus” Gastroenterology 109: 1541-1546.

To meaningfully curb the rising incidence of esophageal adenocarcinoma, effective methods are needed to screen the population for the critical precursor of Barrett's esophagus (BE). Minimally or non-invasive tools have been proposed for BE screening, but have been hampered by lack of optimally sensitive and specific markers. Desired screening markers discriminate BE from normal esophagogastric mucosa. Certain aberrantly methylated genes are associated as candidate markers for BE (see, e.g., Gastroenterology 2011; 140: S-222).

Accordingly, during the development of embodiments of the technology experiments were performed to assess the value of selected methylated DNA markers to discriminate BE from adjacent squamous esophagus (SE) and gastric cardia (GC) and from SE and GC in healthy controls.

Patients with and without known BE were recruited prior to routine upper endoscopy. BE cases had >1 cm length of circumferential columnar mucosa with histologically confirmed intestinal metaplasia; controls had no BE as determined endoscopically. Biopsies were obtained in cases from BE, GC (1 cm below Z-line), and SE (>2 cm above BE) cases, and in controls from GC (as for BE) and SE (5 cm above Z-line), and promptly frozen. Biopsy samples were processed as a batch, and assayed in blinded fashion. Following DNA extraction and bisulfite treatment, methylation on target genes was assayed by methylation-specific PCR for the markers APC, HPP1, SFRP1, and by QuARTS assay for the markers BMP3 and NDRG4. ß-actin was quantified as a control marker for total human DNA.

Among 25 BE cases and 22 controls, the median ages were 67 (range 39-83) and 50 (range 20-78), respectively, and men represented 72% and 46% of the subjects in the BE and control groups, respectively. Median BE length was 6 cm (range 2-14 cm). Except for APC, median levels of methylated markers were significantly and substantially (e.g., 200-1100 times) higher in BE than in adjacent SE and GC or relative to normal SE and GC. Sensitivities for BE at various specificities are shown for each marker (Table 7). Methylated markers were significantly higher in GC adjacent to BE than in GC from normal controls. Methylated APC was higher in BE than SE, but did not distinguish BE from GC. In contrast to methylated markers, ß-actin distributions were similar across tissue groups. Marker levels increased with BE length for NDRG4, SFRP1, BMP3, and HPP1 (p=0.01, 0.01, 0.02, and 0.04, respectively). Factors not significantly affecting marker levels included age, sex, inflammation, and presence of dysplasia (none (8), low grade (6), high grade (11)).

As such, these date demonstrate that the selected methylated DNA markers highly discriminate BE from GC and SE, and provide for useful screening applications.

TABLE 7 Sensitivity for BE, % NDRG4 SFRP1 BMP3 HPP1 APC Specificity Cutoff* 100% 96 96 84 84 0  95% 96 96 92 88 8  90% 96 96 92 92 8 *Based on combined SE and GC data from normal controls

Example 5—Methylated DNA Markers in Pancreatic Juice Discriminate Pancreatic Cancer from Chronic Pancreatitis and Normal Controls

Pancreatic juice analysis has been explored as a minimally-invasive approach to early detection of pancreatic cancer (PC). However, cytology and many molecular markers in pancreatic juice have proved insensitive or failed to distinguish PC from chronic pancreatitis (see, e.g., J Clin Oncol 2005; 23: 4524). Experiments were performed to verify that assay of aberrantly methylated genes may represent a more accurate approach for PC detection from pancreatic juice (see, e.g., Cancer Res 2006; 66: 1208). In particular, data were collected to assess selected methylated DNA markers assayed from pancreatic juice to discriminate case patients with PC from controls with chronic pancreatitis (CP) or a normal pancreas (NP).

A panel of 110 patients (66 PC, 22 CP, 22 NP controls) underwent secretin stimulated pancreatic juice collection during endoscopic ultrasound. Diagnoses were histologically confirmed for PC and radiographically-based for CP and NP. Juice was promptly frozen and stored at −80° C. Assays were performed in blinded fashion on samples thawed in batch. Candidate methylated DNA markers were selected by whole methylome sequencing in a separate tissue study. After DNA was extracted from pancreatic juice and bisulfite treated, gene methylation was determined by methylation-specific PCR for CD1D, CLEC11A, and KCNN2, or by QuARTS for BMP3 and NDRG4. KRAS mutations (7 total) were assayed by QuARTS (presence of any KRAS mutation was considered to be a positive). 8-actin, a marker for human DNA, was also assayed by QuARTS, to provide for control of DNA amount.

Respectively for PC, CP, and NP, the median age was 67 (range 43-90), 64 (range 44-86), and 60 (range 35-78); men represented 56, 68, and 21% of these groups respectively. All markers discriminated PC from NP but to a variable extent. The AUC was 0.91 (95% CI, 0.85-0.97), 0.85 (0.77-0.94), 0.85 (0.76-0.94), 0.78 (0.67-0.89), and 0.75 (0.64-0.87) for methylated CD1D, NDRG4, CLEC11A, KCNN2, and BMP3, respectively, and 0.75 (0.64-0.86) for mutant KRAS. Discrimination for PC by CD1D was significantly higher than by KRAS (p=0.01), KCNN2 (p=0.02), or BMP3 (p<0.01). Positively rates in PC and CP are shown for each marker at 95 and 100% normal specificity cutoffs (Table 8); the positively rate in CP (false-positives) was lowest with CD1D and highest with KRAS. Marker levels were not significantly affected by PC site (head, body, tail) or stage (N0 vs. N1). ß-actin levels were similar across patient groups.

These data show that methylated DNA markers discriminate PC from CP and NP when assayed from pancreatic juice, e.g., secretin-stimulated pancreatic juice. In particular, methylated CD1D was significantly more sensitive for PC and showed substantially fewer false-positives with CP than did mutant KRAS.

TABLE 8 Positivity Rates, % At 95% Specificity* At 100% Specificity* PC CP PC CP Methylation Markers CD1D 75 9 63 5 NDRG4 67 14 56 5 CLEC11A 56 18 38 5 KCNN2 33 18 33 18 BMP3 31 9 23 5 Mutation Marker KRAS 55 41 53 32 *Specificity cutoffs based on NP data

Example 6—Sensitive DNA Marker Panel for Detection of Pancreatic Cancer by Assay in Pancreatic Juice

Pancreatic juice analysis represents a minimally-invasive approach to detection of pancreatic cancer (PC) and precancer. It has been found that specific methylated DNA markers in pancreatic juice discriminate PC from chronic pancreatitis (CP) and normal pancreas (Gastroenterology 2013; 144:S-90), but new markers and marker combinations remain unexplored.

Experiments were performed to assess the value of recently discovered methylated DNA markers and mutant KRAS assayed alone and combined in pancreatic juice to discriminate PC from chronic pancreatitis (CP) and reference controls (CON).

167 patients (85 PC, 30 CP, 23 premalignant intraductal mucinous neoplasm (IPMN), 29 CON) who had undergone secretin stimulated pancreatic juice collection during EUS were studied. Diagnoses were histologically based for PC, radiographically for CP, and histologically or radiographically for IPMN. Specificity was based on CON, which included patients with risk factors for PC, elevated pancreatic enzymes, or GI symptoms but radiographically-normal pancreas. Juice samples archived at −80° C. were blindly batch assayed. On DNA extracted from 200 μL pancreatic juice, gene methylation was determined after bisulfite treatment by quantitative allele-specific real-time target and signal amplification (QuARTS) for assay of ADCY1, CD1D, BMP3, PRKCB, KCNK12, C13ORF18, IKZF1, CLEC11A, TWIST, NDRG4, ELMO, and 55957 Mutant KRAS mutations (7 total) and 8-actin (a marker for total human DNA) were also assayed by QuARTS. From quantitative data, an algorithm was followed to achieve optimal discrimination by a panel combining all markers.

Respectively for PC, CP, IPMN, and CON: median age was 67 (IQR 60-77), 66 (55-77), 66 (60-76) and 70 (62-77); men comprised 52, 53, 49, and 72%. At respective specificity cutoffs of 90% and 95%: the combined marker panel achieved highest PC sensitivities (88% and 77%); ADCY1, the most sensitive single marker, detected 84% and 71%. Other single markers detected PC but to variable extents (table). Overall discrimination by area under ROC curve was higher by panel than by any single marker (p<0.05), except ADCY1 (table). At 90% specificity, panel detected 44% of all IPMNs and 75% (¾) of subset with high grade dysplasia. Positivity rates were substantially lower in CP than in PC for all markers shown in Table 7 (p<0.0001). At 100% specificity, the panel was positive in 58% PC, 17% IPMN, and 13% CP. Accordingly, these results demonstrate that a panel of novel methylated DNA markers and mutant KRAS assayed from pancreatic juice achieves high sensitivity for PC.

TABLE 7 Marker positivity rates in pancreatic juice of patients with pancreatic cancer (PC), intraductal papillary mucinous neoplasm (IPMN), and chronic pancreatitis (CP). Positivity Rates, % AUC At 90% Specificity* At 95% Specificity* (PC vs Con) PC IPMN CP PC IPMN CP Methylation Markers** ADCY1 0.89 84 39 30 71 35 17 C13ORF18 0.82 67 17 13 52 13 3 PRKCB 0.82 62 9 20 42 4 17 CD1D 0.82 61 22 10 46 17 10 KCNK12 0.82 54 17 10 25 13 3 BMP3 0.81 49 13 7 27 13 0 IKZF1 0.80 71 22 20 54 22 17 Mutation Marker KRAS 0.80 59 22 13 58 17 10 All Markers Panel*** 0.91 88 44 37 77 39 23 *Specificity cutoffs based on reference control (CON) data. **Top 7 individual methylated DNA markers shown. ***Except for ADCY1, the Panel had significantly higher AUC than individual methylated DNA markers (p < 0.05).

Example 7: Detecting Pancreatic Cancer within Stool Sample Using CD1D Marker

Stool samples from 45 individuals having pancreatic cancer and 45 individuals not having pancreatic cancer were collected and tested for the presence of the CD1D marker. Pancreatic cancer was successfully detected using CD1D marker from stool.

Example 8: Novel DNA Methylation Markers Associated with Early-Stage Pancreatic Cancer

Study Overview:

In independent case-control tissue studies, experiments were performed to identify novel and highly discriminant methylation markers for PanC using RRBS for the discovery phase and methylation-specific PCR (MSP) for the validation phase.

Study Population:

After approval by the Mayo Clinic Institutional Review Board, tissue samples were identified from existing cancer registries. The accessible population included those who underwent distal pancreatectomy, pancreaticoduodenectomy, colectomy or colon biopsy with a frozen archived specimen. All tissues were reviewed by an expert gastrointestinal pathologist to confirm correct classification. The PanC case samples included pancreatic ductal adenocarcinoma tissues limited to early-stage disease (AJCC stage I and II) (Edge SBB, D. R.; Compton, C. C.; Fritz, A. G.; Greene, F. L.; Trotti, A. (Eds.), editor. AJCC Cancer Staging Manual. 7th ed: Springer, New York; 2010). Neoplasms arising from IPMN lesions were excluded. There were two control groups studied. The first, termed “normal pancreas,” included the histologically normal resection margins of low risk (serous cystadenoma) or focal pancreatic neoplasms (neuroendocrine tumors). The second control group included colonic epithelial tissues from patients confirmed to be free from PanC or colonic neoplasm. Cases and both controls were matched by sex, age (in 5-year increments) and smoking status (current or former vs. never). In a central core laboratory, case and control tissues were microdissected and DNA was extracted using a phenol-chloroform technique, yielding at least 500 ng of DNA. Case identification, matching and DNA extraction were performed by independent personnel to maintain blinding of laboratory personnel to case and control status.

Reduced Representation Bieulfite Sequencing:

Library preparation (Gu H, Bock C, Mikkelsen T S, Jager N, Smith Z D, Tomazou E, et al. Genome-scale DNA methylation mapping of clinical samples at single-nucleotide resolution. Nat Methods. 2010; 7:133-6): Genomic DNA (300 ng) was fragmented by digestion with 10 Units of MspI, a methylation-specific restriction enzyme which recognizes CpG containing motifs. This enriches the samples for CpG content and eliminates redundant areas of the genome. Digested fragments were end-repaired and A-tailed with 5 Units of Klenow fragment (3′-5′ exo-), and ligated overnight to methylated TruSeq adapters (Illumina, San Diego Calif.) containing one of four barcode sequences (to link each fragment to its sample ID.) Size selection of 160-340 bp fragments (40-220 bp inserts) was performed using Agencourt AMPure XP SPRI beads/buffer (Beckman Coulter, Brea Calif.). Buffer cutoffs were 0.7× to 1.1× sample volumes of beads/buffer. Final elution volume was 22 uL (EB buffer—Qiagen, Germantown Md.) qPCR was used to gauge ligation efficiency and fragment quality on a small aliquot of sample. Samples then underwent bisulfite conversion (twice) using a modified EpiTect protocol (Qiagen). qPCR and conventional PCR (PfuTurbo Cx hotstart-Agilent, Santa Clara Calif.) followed by Bioanalyzer 2100 (Agilent) assessment on converted sample aliquots determined the optimal PCR cycle number prior to amplification of the final library. Conditions for final PCR: 50 uL r×n: 5 uL of 10× buffer, 1.25 uL of 10 mM each dNTP's, 5 uL primer cocktail (˜5 uM), 15 uL template (sample), 1 uL PfuTurbo Cx hotstart, 22.75 water. 95C-5 min; 98C-30 sec; 16 cycles of 98C-10 sec, 65C-30 sec, 72C-30 sec; 72C-5 min; 4C. Samples were combined (equimolar) into 4-plex libraries based on the randomization scheme and tested with the bioanalyzer for final size verification, and with qPCR using phiX standards and adaptor-specific primers.

Sequencing and Bioinformatics:

Samples were loaded onto flow cell lanes according to a randomized lane assignment with additional lanes reserved for internal assay controls. Sequencing was performed by the Next Generation Sequencing Core at the Mayo Clinic Medical Genome Facility on the Illumina HiSeq 2000. Reads were unidirectional for 101 cycles. Each flow cell lane generated 100-120 million reads, sufficient for a median coverage of 30-50 fold sequencing depth (read number per CpG) for aligned sequences. Standard Illumina pipeline software was used for base calling and sequence read generation in the fastq format. As described previously (Sun Z, Baheti S, Middha S, Kanwar R, Zhang Y, Li X, et al. SAAP-RRBS: streamlined analysis and annotation pipeline for reduced representation bisulfite sequencing. Bioinformatics. 2012; 28:2180-1), SAAP-RRBS, a streamlined analysis and annotation pipeline for reduced representation bisulfite sequencing, was used for sequence alignment and methylation extraction.

Validation Studies by Methylation-Specific PCR:

Overview: Two MSP-based validation studies were performed on expanded sample sets to confirm the accuracy and reproducibility of the observed differentially methylated candidates. The first, an internal validation study, was performed on unmatched, unblinded samples using biological and technical replicates of PanC and normal colon and technical replicates of normal pancreas. This step was performed to ensure that the sites of differential methylation identified by the RRBS data filtration, where % methylation was the unit of analysis, would be reflected in MSP, where the unit of analysis is the absolute genomic copy number of the target sequence, corrected by the concentration of input DNA for each sample. The second, external validation experiment, utilized MSP to test the top candidates in randomly allocated, matched, blinded, independent PanC, benign pancreas and normal colon samples.

Primer design: Primers for each marker were designed to target the bisulfite-modified methylated sequences of each target gene (IDT, Coralville Iowa) and a region without cytosine-phosphate-guanine sites in the ß-actin gene, as a reference of bisulfite treatment and DNA input. The design was done by either Methprimer software (University of California, San Francisco Calif.) or by semi-manual methods (by H.Z and W.R.T). Assays were then tested and optimized by running qPCR with SYBR Green (Life Technologies, Grand Island N.Y.) dyes on dilutions of universally methylated and unmethylated genomic DNA controls.

Methylation specific PCR: MSP reactions were performed on tissue-extracted DNA as previously described (Kisiel J B, Yab T C, Taylor W R, Chari S T, Petersen G M, Mahoney D W, et al. Stool DNA testing for the detection of pancreatic cancer: assessment of methylation marker candidates. Cancer. 2012; 118:2623-31). Briefly, DNA was bisulfite treated using the EZ DNA Methylation Kit (Zymo Research, Orange, Calif.) and eluted in buffer. One μl bisulfite-treated DNA was used as a template for methylation quantification with a fluorescence-based real-time PCR, performed with SYBR Green master mix (Roche, Mannheim Germany). Reactions were run on Roche 480 LightCyclers (Indianapolis, Ind.), where bisulfite-treated CpGenome Universal Methylated DNA (Millipore, Billerica, Mass.) was used as a positive control, and serially diluted to create standard curves for all plates. Oligonucleotide sequences and annealing temperatures are available upon request.

Statistical Analysis

RRBS: The primary comparison of interest was the methylation difference between cases and pancreatic controls at each mapped CpG. CpG islands are biochemically defined by an observed to expected CpG ratio exceeding 0.6 (Gardiner-Garden M, Frommer M. CpG islands in vertebrate genomes. Journal of molecular biology 1987; 196:261-82). However, for this model, tiled units of CpG analysis “differentially methylated region (DMR)” were created based on the distance between CpG site locations for each chromosome. As the distance between any given CpG exceeded the previous or next location by more than 100 bps, a new island identifier was created. Islands with only a single CpG were excluded. The secondary outcome was the same comparison between cases and colon controls. Individual CpG sites were considered for differential analysis only if the total depth of coverage per disease group was ≥200 reads (roughly equating to an average of 10 reads per subject) and the variance of % methylation was greater than zero (non-informative CpG sites with 0 variance were excluded). The criteria for read depth were based on the desired statistical power to detect a difference of 10% in the methylation rate between any two groups in which the sample size of individuals for each group was 18.

Statistical significance was determined by logistic regression on the % methylation per DMR (using the actual counts) with the groups defined as PanC, normal pancreas, and normal colon. To account for varying read depths across individual subjects, an over-dispersed logistic regression model was used, where dispersion parameter was estimated using the Pearson Chi-square statistic of the residuals from fitted model. To assess strand specific methylation, forward and reverse regions were analyzed separately. The DMRs were then ranked according to their significance level and were considered as a viable marker region if the methylation rate in the controls was ≤1% but ≥10% in PanC. Each significant DMR was considered as a candidate marker.

For the internal validation study, the primary outcome was the area under the receiver operating characteristics curve (AUC) for each marker. This was calculated using logistic regression (JMP version 9.0.1, SAS Institute, Cary N.C.) to model the strength of the concentration-corrected copy number of each marker with PanC in comparison to normal pancreas and normal colon. The markers with the highest AUC values and widest ratio of median genomic copy number between cases and controls were selected for the external validation study. The primary outcome for the external validation experiment was the AUC for each marker plotted against the signal strength of each marker, measured by the log of the ratio of median corrected copy number in cases compared to controls. With eighteen cases there is >80% power to detect an area under the curve of 0.85 or higher from the null hypothesis of 0.5 at a two-sided significance level 0.05. The secondary endpoint was the AUC of two-marker combinations, measured by logistic regression, in which both markers were required to independently associate with PanC cases.

RRBS Marker Discovery

Matched, blinded, randomly allocated DNA extracts from 18 pancreatic cancer tumors, 18 benign pancreatic control tissues and 18 normal colon epithelial tissues were sequenced by RRBS. Median age was 61 (interquartile range 52-65), 61% were women, and 44% were current or former smokers. A total of 6,101,049 CpG sites were captured in any of the samples with at least 10× coverage. After selecting only CpG sites where group coverage and variance criteria were met, a total of 1,217,523 CpG sites were further considered for analysis. Approximately 500 DMRs met significance criteria for differential methylation. Among these, we identified 107 candidate regions with sufficient methylation signatures for MSP primer design. Methylation signatures ranged from 3 neighboring CpGs to 52 CpGs. Methylation levels of the pancreatic cancers rarely exceeded 25% at filtered CpGs, reflecting high levels of contaminating stromal cells. This was confirmed after sequencing each of the cancers for KRAS mutations to verify allele frequencies for the positive samples; for the 50% of PanC specimens which harbored a heterozygous KRAS base change, the frequency of the mutant allele was at least 4 times less than the corresponding wild-type allele.

Internal Validation

Based on the number of neighboring CpGs in each candidate gene methylation signature, primers were designed for 87 of the 107 candidate markers. MSP was then used to assay the candidates in sample of DNA from an additional 20 unblinded PanC lesions, 10 additional normal colonic epithelial samples (biologic replicates) as well as, remaining DNA samples from the 18 sequenced PanC lesions, 15 of the sequenced benign pancreatic tissues and 10 of the sequenced normal colon samples (technical replicates). With first-pass primer designs, 74 of 87 markers successfully amplified. With re-design, the remaining 13 primers successfully amplified and were tested in 12 unblinded PanC samples and 10 normal colon samples. ß-actin amplified in all samples. With either first or second-pass MSP, 31 of 87 candidate markers had an AUC>0.85. Based on the magnitude of difference in median genomic copy number between cases and controls for each candidate marker, 23 were selected for external validation in independent samples. These were ABCB1, ADCY1, BMP3, C13ORF18, CACNA1C, CD1D, CHR12:133484978-133485738 (CHR12 133), CLEC11A, ELMO1, FOXP2, GRIN2D, IKZF1, KCNK12, KCNN2, NDRG4, PRKCB, RSPO3, SCARF2, SHH, SLC38A3, TWIST1, VWC3 and WT1.

External Validation

Matched, blinded, randomly allocated DNA from 18 PanC, 18 benign pancreatic and 36 normal colon epithelial samples were assayed by MSP for the 23 top candidates. The median age of this subset was 60 (interquartile range 54-64). The majority (55%) of samples came from men and 61% were current or former smokers. ß-actin amplified in all samples. 9 of 23 candidates showed excellent association with PanC. The individual AUC values for CACNA1C, CHR12.133, WT1, GRIN2D, ELMO1, TWIST1, C13ORF18, KCNN2, and CLEC11A were 0.95, 0.95, 0.94, 0.94, 0.93, 0.92, 0.91, 0.90 and 0.90, respectively. Good association was seen with 9 other candidates; the AUC values for PRKCB, CD1D, SLC38A3, ABCB1, KCNK12, VWC2, RSPO3, SHH and ADCY1 were 0.89, 0.88, 0.86, 0.86, 0.86, 0.85, 0.85, 0.85 and 0.84 respectively.

The log ratio of the median case and control values for each marker was plotted against the AUC. Eight markers, SHH, KCNK12, PRKCB, CLEC11, C13ORF18, TWIST1, ELMO1 and CHR12.133 each had an AUC greater than 0.85, and showed greater than 1.5 log (>30-fold) greater genomic copy number among cases than controls. KCNK12, PRKCB, ELMO1 and CHR12.133 showed greater than 2 log (>100-fold) difference.

Complementarity Analysis

Among all 231 possible 2-marker combinations, both markers remained highly significant in 30 (13%) pair-wise models of association with PanC. Of those, 18 (8%) showed improvement of the AUC. Noteworthy among several complementary markers, C13ORF8 improved the accuracy of CACNA1C, WT1, GRIND2D, SLC38A3 and SCARF2 with AUCs of 0.99, 0.99, 0.97, 0.96, and 0.95, respectively, for each combination. Though the AUC for SHH as an individual marker was 0.85, it improved the performance of 6 other markers when paired. The AUC of CACNA1C, WT1, SLC38A3, ABCB1, VWC2 and RSPO3improved to 0.96, 0.95, 0.92, 0.98, 0.88 and 0.95, respectively when combined in models with SHH. Of the 18 most robust marker combinations, 9 combinations could be tested in pair-wise comparisons from the internal validation data set. Of these, 7 pairs (78%) remained highly significant in both data sets.

Example 9: Highly Discriminant Methylated DNA Markers for Detection of Barrett's Esophagus

To curb the rising incidence of esophageal adenocarcinoma, effective methods are needed to screen the population for the critical precursor—Barrett's esophagus (BE). Minimally or non-invasive tools have been proposed but hampered by lack of optimally sensitive and specific markers. Experiments were performed and aberrantly methylated BMP3 and NDRG4 were identified as discriminant candidate markers for BE.

An aim of such experiments was to prospectively assess the accuracy of methylated BMP3 and NDRG4 to identify BE using endoscopic biopsies (Phase 1) and brushings from the whole esophagus and cardia to simulate non-endoscopic sampling devices (Phase 2).

Cases with and controls without BE were recruited prior to endoscopy. BE cases had >1 cm of circumferential columnar mucosa with confirmed intestinal metaplasia; controls had no BE endoscopically. In Phase 1, biopsies were obtained in cases from BE, gastric cardia ((GC); 1 cm below Z-line) and squamous epithelium ((SE); >2 cm above BE) and in controls from GC (as for BE) and SE (5 cm above Z-line); then promptly frozen. Biopsy samples were processed as a batch, and assayed in blinded fashion. In Phase 2, specimens were obtained using a high capacity endoscopic cytology brush (Hobbs Medical, Stafford Springs Conn.); the cardia, BE (in cases), and full esophageal length were brushed to simulate a swallowed sponge sampling device. Following DNA extraction and bisulfite treatment, methylation on target genes was assayed by quantitative allele-specific real-time target and signal amplification. 8-actin was also quantified as a marker for total human DNA.

100 subjects were prospectively studied. Phase 1: Among 40 BE cases and 40 controls: median age was 65 (quartiles 55-77) and 54 (37-69) and men comprised 78% and 48%, respectively. Median BE length was 6 cm (range 3-10). Median levels of methylated markers were substantially higher (34-600 times) in BE than in adjacent SE and GC or than in normal SE and GC (Table). In contrast to methylated markers, ß-actin distributions were similar across tissue groups. Both marker levels increased with BE length and age, p<0.001 whereas only NDRG4 increased significantly with presence of dysplasia (none (19), low grade (9), high grade (11); p=0.003). Factors not significantly affecting marker levels included sex and inflammation. Phase 2: Among 10 BE cases and 10 controls, median age was 64 (59-70) and 66 (49, 71) and men comprised 80 and 30% respectively. Median BE length was 2 cm (range 1-4). Discrimination of BE by markers was extraordinary with AUC of 1.0 for NDRG4 and 0.99 for BMP3; levels were >100 times higher in cases than controls (FIG. 2).

These experiments demonstrate that selected methylated DNA markers highly discriminate BE from normal GC and SE, both in biopsy and brushed specimens. Table 9 shows the function and cancer biology associations of the selected methylated DNA markers.

TABLE 8 Marker levels (copy numbers of markers adjusted for beta actin) for BMP3 and NDRG4 biopsies from BE cases (cardia, Barrett's, squamous) and controls (cardia, squamous). BMP3 NDRG4 Normal Barrett's Normal Barrett's controls cases controls cases Squamous 0.8 5.6 1.0 4.9 Q1, Q3 0.3, 2.2 0.7, 14.8 0.5, 2.7 1.5, 10.9 P90, P95 7.0, 23.0 25.5, 50.3 5.0, 13.7 32.0, 64.1 BE 300.2 390.6 Q1, Q3 137.1, 659.5 146.6, 763.5 P90, P95 1083.1, 1219.0 921.8, 1006.6 Cardia 0.5 8.2 2.3 11.5 Q1, Q3 0.3, 1.9 2.8, 40.3 1.0, 6.3 5.0, 48.3 P90, P95 10.3, 16.4 190.7, 431.5 13.1, 15.4 116.7, 345.0 Composite 1.3 131.4 2.3 136.5 Q1, Q3 0.4, 3.8 67.1, 242.7 1.1, 5.3 68.9, 272.3 P90, P95 10.0, 15.3 402.9, 417.9 8.1, 12.5 344.0, 383.3 Pvalue <0.0001 <0.0001

TABLE 9 Function and cancer biology associations of top candidate markers Reference (complete Protein Cancer reference DMR Symbol Gene name Function association below table) Chr7: ABCB1 ATP-binding Membrane- Multi-drug Lee, et al. 87229775- cassette, sub- associated resistance to 2013 87229856 family B, transporter chemotherapy member 1 protein Chr7: ADCY1 Adenylate Transmembrane Methylation Vincent, et al. 45613877- cyclase 1 signalling associated with 2011 45614564 pancreatic cancer Chr13: C13ORF18 KIAA0226-like Uncharacterized Methylation Vincent, et al. 46960770- associated with 2011; Yan, et 46961464 pancreatic al. 2009 cancer, cervical neoplasia Chr12: CACNA1C Calcium Mediates cellular Methylation Vincent, et al. 2800665- channel, calcium ion influx associated with 2011 2800898 voltage- pancreatic dependent, L cancer type, alpha 1C subunit Chr1: CD1D CD1D molecule Transmembrane Target for novel Liu, et al. 158150797- glycoprotein immunotherapy- 158151142 mediating based cancer presentation of treatment; antigens to T expressed by cells medulloblastoma Chr19: CLEC11 C-type lectin-11 C-type lectin None — 51228217- domain, 51228703 uncharacterized Chr12: (Chr12-133) — Uncharacterized — — 133484978- 133485738 Chr7: ELMO1 Engulfment and Interaction with Promotion of Li, et al. 37487539- cell motility 1 cytokinesis metastatic 37488498 proteins, spread promotion of cell motility and phagocytosis Chr7: FOXP2 Forkhead box Transcription Expressed in Stumm, et 113727624- P2 factor, expressed subsets of al. Campbell, 113727693 in brain, lung, gut prostate cancer, et al. lymphoma and multiple myeloma Chr19: GRIN2D Glutamate NMDA receptor, Methylation Vincent, et al. 48946755- receptor, neurotransmission associated with 2011, Jiao, et 48946912 ionotropic, N- pancreatic al. methyl D- cancer, mutant aspartate 2 D in breast cancer Chr7: IKZF1 IKAROS family DNA binding Mutant in Asai, et al. 50343848- zinc finger 1 protein leukemias 50343927 associated with chromatin remodeling Chr2: KCNK12 Potassium Non-functioning Methylation Vincent, et al. 47797332- channel, sub- potassium associated with 2011, Kober, 47797371 family K, channel pancreatic and et al. member 12 colon cancer Chr5: KCNN2 Potassium Potassium Overexpressed Camões, et al. 113696984- intermediate/ channel, voltage- in prostate 113697057 small gated, calcium cancer conductance activated calcium-activated channel, subfamily N, member 2 Chr16: NDRG4 N-myc Cytosolic Methylated in Kisiel, et al., 58497395- downregulated signalling protein pancreatic, Ahlquist, et al. 58497458 gene, family required for cell colon cancer member 4 cycle progression Chr16: PRKCB Protein kinase Serine- and Methylation Vincent, et al. 23846964- C, beta threonine specific associated with 2011, Surdez, 23848004 kinase involved in pancreatic et al. cell signalling cancer, druggable target in Ewing sarcoma Chr6: RSPO3 R-spondin, type Regulatory Methylation Vincent, et al. 127440526- 3 protein in Wnt/β- associated with 2011, 127441039 catenin signalling pancreatic Seshigiri, et pathway cancer, elevated al. expression in colon cancers Chr22: SCARF2 Scavenger Mediates binding Methylation Vincent, et al. 20785373- receptor class and degradation associated with 2011, Zhao, et 20785464 F, member 2 of low density pancreatic al. lipoproteins cancer, methylation and reduced expression in gastric cancer Chr7: SHH Sonic Embryogenesis Methylation Vincent, et al. 155597771- hedgehog associated with 2011, Gurung, 155597951 pancreatic et al. cancer, epigenetically repressed in MEN1 syndrome; hedgehog signalling mediates pancreatic cancer invasion Chr3: SLC38A3 Solute carrier, Uncharacterized Decreased Person, et al. 50243467- family 38, expression in 50243553 member 3 lung cancer Chr7: TWIST1 Twist basic Transcription Methylation Vincent, et al. 19156788- helix-loop-helix factor expressed associated with 2011, Shin, et 19157093 transcription in placental and pancreatic al. factor 1 mesodermal cancer, biliary tissue cancer, urothelial cancer Chr7: VWC2 von Willebrand Secreted bone Methylation Vincent, et al. 49813182- factor C morphogenic associated with 2011 49814168 domain protein antagonist pancreatic containing 2 cancer Chrl11: WT1 Wilms tumor 1 Zinc finger motif Methylation Vincent, et al. 32460759- transcription associated with 2011, Jacobs, 32460800 factor pancreatic, et al. prostate, ovarian and breast cancers

Lee W K, Chakraborty P K, Thevenod F. Pituitary homeobox 2 (PITX2) protects renal cancer cell lines against doxorubicin toxicity by transcriptional activation of the multidrug transporter ABCB1. International journal of cancer Journal international du cancer. 2013; 133:556-67.

Vincent A, Omura N, Hong S M, Jaffe A, Eshleman J, Goggins M. Genome-wide analysis of promoter methylation associated with gene expression profile in pancreatic adenocarcinoma. Clinical cancer research: an official journal of the American Association for Cancer Research. 2011; 17:4341-54.

Yang N, Eijsink J J, Lendvai A, Volders H H, Klip H, Buikema H J, et al. Methylation markers for CCNA1 and C13ORF18 are strongly associated with high-grade cervical intraepithelial neoplasia and cervical cancer in cervical scrapings. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2009; 18:3000-7.

Liu D, Song L, Brawley V S, Robison N, Wei J, Gao X, et al. Medulloblastoma expresses CD1d and can be targeted for immunotherapy with NKT cells. Clin Immunol. 2013; 149:55-64.Li H, Yang L, Fu H, Yan J, Wang Y, Guo H, et al. Association between Galphai2 and ELMO1/Dock180 connects chemokine signalling with Rac activation and metastasis. Nat Commun. 2013; 4:1706.

Stumm L, Burkhardt L, Steurer S, Simon R, Adam M, Becker A, et al. Strong expression of the neuronal transcription factor FOXP2 is linked to an increased risk of early PSA recurrence in ERG fusion-negative cancers. Journal of clinical pathology. 2013; 66:563-8.

Campbell A J, Lyne L, Brown P J, Launchbury R J, Bignone P, Chi J, et al. Aberrant expression of the neuronal transcription factor FOXP2 in neoplastic plasma cells. British journal of haematology. 2010; 149:221-30.

Jiao X, Wood L D, Lindman M, Jones S, Buckhaults P, Polyak K, et al. Somatic mutations in the Notch, NF-KB, PIK3CA, and Hedgehog pathways in human breast cancers. Genes, chromosomes & cancer. 2012; 51:480-9.

Asai D, Imamura T, Suenobu S, Saito A, Hasegawa D, Deguchi T, et al. IKZF1 deletion is associated with a poor outcome in pediatric B-cell precursor acute lymphoblastic leukemia in Japan. Cancer Med. 2013; 2:412-9.

Kober P, Bujko M, Oledzki J, Tysarowski A, Siedlecki J A. Methyl-CpG binding column-based identification of nine genes hypermethylated in colorectal cancer. Molecular carcinogenesis. 2011; 50:846-56.

Camoes M J, Paulo P, Ribeiro F R, Barros-Silva J D, Almeida M, Costa V L, et al. Potential downstream target genes of aberrant ETS transcription factors are differentially affected in Ewing's sarcoma and prostate carcinoma. PLoS ONE. 2012; 7:e49819.

Kisiel J B, Yab T C, Taylor W R, Chari S T, Petersen G M, Mahoney D W, et al. Stool DNA testing for the detection of pancreatic cancer: assessment of methylation marker candidates. Cancer. 2012; 118:2623-31.

Ahlquist D A, Zou H, Domanico M, Mahoney D W, Yab T C, Taylor W R, et al. Next-Generation Stool DNA Test Accurately Detects Colorectal Cancer and Large Adenomas. Gastroenterology. 2012; 142:248-56.

Surdez D, Benetkiewicz M, Perrin V, Han Z Y, Pierron G, Ballet S, et al. Targeting the EWSR1-FLI1 oncogene-induced protein kinase PKC-beta abolishes ewing sarcoma growth. Cancer research. 2012; 72:4494-503.

Seshagiri S, Stawiski E W, Durinck S, Modrusan Z, Storm E E, Conboy C B, et al. Recurrent R-spondin fusions in colon cancer. Nature. 2012; 488:660-4.

Zhao J, Liang Q, Cheung K F, Kang W, Lung R W, Tong J H, et al. Genome-wide identification of Epstein-Barr virus-driven promoter methylation profiles of human genes in gastric cancer cells. Cancer. 2013; 119:304-12.

Gurung B, Feng Z, Iwamoto D V, Thiel A, Jin G, Fan C M, et al. Menin epigenetically represses Hedgehog signaling in MEN1 tumor syndrome. Cancer research. 2013; 73:2650-8.

Person R J, Tokar E J, Xu Y, Orihuela R, Ngalame N N, Waalkes M P. Chronic cadmium exposure in vitro induces cancer cell characteristics in human lung cells. Toxicol Appl Pharmacol. 2013.

Shin S H, Lee K, Kim B H, Cho N Y, Jang J Y, Kim Y T, et al. Bile-based detection of extrahepatic cholangiocarcinoma with quantitative DNA methylation markers and its high sensitivity. The Journal of molecular diagnostics: JMD. 2012; 14:256-63.

Jacobs D I, Mao Y, Fu A, Kelly W K, Zhu Y. Dysregulated methylation at imprinted genes in prostate tumor tissue detected by methylation microarray. BMC Urol. 2013; 13:37.

Example 9: A Stool-Based microRNA and DNA Marker Panel for the Detection of Pancreatic Cancer

Given the extraordinary lethality of pancreatic cancer (PC), practical non-invasive methods for pre-symptomatic screen detection are needed. MicroRNAs (miRNAs) have altered expression in PC.

Experiments were performed with having an aim to explore the feasibility of stool miR-1290 for detection of PC.

Archival stool samples from 58 PC cases and 64 healthy controls matched on age, gender, and smoking history were analyzed. Detection of miRNA was performed by a stem-loop quantitative reverse transcription polymerase chain reaction (qRT-PCR) approach. Quantitation of miRNA was based on measuring the absolute copies per nanogram of extracted RNA. DNA markers (methylated BMP3, mutant KRAS and ß-actin) were hybrid captured and amplified as described (Cancer 2012, 118:2623). A step-wise logistic regression model, limited to 5 variables, was used to build an optimized marker panel based on miR-1290, DNA markers, and age. The age adjusted areas under the ROC curve (AUCs) for each of the models were compared using the methods of DeLong et al. The association of miR-1290 with clinical factors was assessed using the Wilcoxon Rank Sums test.

Distributions of miR-1290 were significantly higher in stools from PC cases than from controls (P=0.0002). Stool miR-1290 levels were not affected by age, sex, tumor site or tumor stage. AUC of stool miR-1290 was 0.74 (95% CI: 0.65-0.82, FIG. 3) for PC detection compared to an AUC of 0.81 (0.73-0.89) by the stool DNA marker panel. The addition of miR-1290 to DNA markers proved incremental (P=0.0007) with an AUC of 0.87 (0.81-0.94). Adding miR-1290 to the DNA panel increased the sensitivity of the test across the entire range of specificities including the critical region of 90-100%. PC sensitivity of the combined marker panel was 64% (50%-76%) at 95% (87%-99%) specificity, and 79% (67%-89%) at 85% (74%-92%) specificity.

These experiments identified stool miR-1290 as a marker for PC.

Example 11—Identifying Markers Using RRBS

During the development of the technology provided herein, data were collected from a case-control study to demonstrate that a genome-wide search strategy identifies novel and informative markers.

Study Population, Specimen Acquisition, and Samples

The target population was patients with pancreas cancer seen at the Mayo Clinic. The accessible population includes those who have undergone a distal pancreatectomy, a pancreaticoduodenectomy, or a colectomy with an archived resection specimen and a confirmed pathologic diagnosis. Colonic epithelial DNA was previously extracted from micro-dissected specimens by the Biospecimens Accessioning Processing (BAP) lab using a phenol-chloroform protocol. Data on the matching variables for these samples were used by Pancreas SPORE personnel to select tissue registry samples. These were reviewed by an expert pathologist to confirm case and control status and exclude case neoplasms arising from IPMN, which may have different underlying biology. SPORE personnel arranged for BAP lab microdissection and DNA extraction of the pancreatic case and control samples and provided 500 ng of DNA to lab personnel who were blinded to case and control status. Archival nucleic acid samples included 18 pancreatic adenocarcinomas, 18 normal pancreas, and 18 normal colonic epithelia matched on sex, age, and smoking status.

The sample types were:

-   -   1) Mayo Clinic Pancreas SPORE registry PanC tissues limited to         AJCC stage I and II;     -   2) control pancreata free from PanC;     -   3) archived control colonic epithelium free from PanC; and     -   4) colonic neoplasm from which DNA had been extracted and stored         in the BAP lab.         Cases and controls were matched by sex, age (in 5-year         increments), and smoking status (current or former vs. never).

Methods

Libraries were prepared according to previously reported methods (see, e.g., Gu et al (2011) “Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling” Nature Protocols 6: 468-81) by fragmenting genomic DNA (300 ng) by digestion with 10 units of MspI a methylation-specific restriction enzyme that recognizes CpG containing motifs. This treatment enriches the samples for CpG content and eliminates redundant areas of the genome. Digested fragments were end-repaired and A-tailed with 5 units of Klenow fragment (3′-5′ exo) and ligated overnight to Illumina adapters containing one of four barcode sequences to link each fragment to its sample ID. Size selection of 160-340 bp fragments (having 40-220 bp inserts) was performed using SPRI beads/buffer (AMPure XP, Beckman Coulter). Buffer cutoffs were from 0.7× to 1.1× of the sample volume of beads/buffer. Samples were eluted in a volume of 22 μl (EB buffer, Qiagen). qPCR was used to gauge ligation efficiency and fragment quality on a small aliquot of sample. Samples then underwent two rounds of bisulfite conversion using a modified EpiTect protocol (Qiagen). qPCR and conventional PCR (Pfu Turbo Cx hotstart, Agilent), followed by Bioanalyzer 2100 (Agilent) assessment on converted sample aliquots, determined the optimal PCR cycle number prior to amplification of the final library. The final PCR was performed in a volume of 50 μl (5 μl of 10×PCR buffer; 1.25 μl of each dNTP at 10 mM; 5 μl of a primer cocktail at approximately 5 μM, 15 μl of template (sample), 1 μl PfuTurbo Cx hotstart, and 22.75 μl water. Thermal cycling began with initial incubations at 95° C. for 5 minutes and at 98° C. for 30 seconds followed by 16 cycles of 98° C. for 10 seconds, 65° C. for 30 seconds, and at 72° C. for 30 seconds. After cycling, the samples were incubated at 72° C. for 5 minutes and kept at 4° C. until further workup and analysis. Samples were combined in equimolar amounts into 4-plex libraries based on a randomization scheme and tested with the bioanalyzer for final size verification. Samples were also tested with qPCR using phiX standards and adaptor-specific primers.

For sequencing, samples were loaded onto flow cell lanes according to a randomized lane assignment with additional lanes reserved for internal assay controls. Sequencing was performed by the NGS Core at Mayo's Medical Genome Facility on the Illumina HiSeq 2000. Reads were unidirectional for 101 cycles. Each flow cell lane generated 100-120 million reads, sufficient for a median coverage of 30× to 50× sequencing depth (based on read number per CpG) for aligned sequences. Standard Illumina pipeline software was used to analyze the reads in combination with RRBSMAP (Xi, et al. (2012) “RRBSMAP: a fast, accurate and user-friendly alignment tool for reduced representation bisulfite sequencing” Bioinformatics 28: 430-432) and an in-house pipeline (SAAP-RRBS) developed by Mayo Biomedical and Statistics personnel (Sun et al. (2012) “SAAP-RRBS: streamlined analysis and annotation pipeline for reduced representation bisulfite sequencing” Bioinformatics 28: 2180-1). The bioinformatic analyses consisted of 1) sequence read assessment and clean-up, 2) alignment to reference genome, 3) methylation status extraction, and 4) CpG reporting and annotation.

Statistical Considerations:

The primary comparison of interest is methylation differences between cases and disease controls at each CpG and/or tiled CpG window. The secondary outcome is the same comparison between cases and normal buffy coat and colon controls. Markers were tested for differential methylation by:

-   -   1. Assessing the distributions of methylation percentage for         each marker and discarding markers with more than 2.5%         methylated background in colon controls and normal buffy coat     -   2. Testing the distribution of methylation of remaining markers         between cases and controls using the Wilcoxon rank sum test and         ranking markers by p-values.     -   3. Using Q-values to estimate the False Discovery Rates (FDR)         (Benjamini et al. (1995) “Multiple Testing” Journal of the Royal         Statistical Society. Series B (Methodological) 57: 289-300;         Storey et al. (2003) “Statistical significance for genomewide         studies” Proc Natl Acad Sci USA 100: 9440-5). At the         discovery-level, an FDR up to 25% is acceptable.

Analysis of Data

A data analysis pipeline was developed in the R statistical analysis software package (“R: A Language and Environment for Statistical Computing” (2012), R Foundation for Statistical Computing). The workflow comprised the following steps:

-   -   1. Read in all CpG sites     -   2. Considered only those CpG sites where the total group depth         of coverage was 200 reads or more. This is based on the power         assessment to detect a difference between 20% and 30%         methylation between any two groups; anything less has little         chance of significance. So, if there are 18 subjects per group         and each subject has 12 reads, the group depth of coverage is         12*18=216.     -   3. Excluded all the CpG sites where the variance of the %         methylation across the groups was 0 (non-informative CpG sites).     -   4. Performed an over-dispersed logistic regression on the %         methylation (using the actual counts) with the groups defined as         Normal Colon/Buffy coat, disease specific control, and specific         cancer of interest (cases) to determine the statistical         significance of the % methylation for the primary and secondary         analyses. An over-dispersed logistic model was used since the         variability in the % methylation between subjects is larger than         what the binomial assumption allows. This dispersion parameter         was estimated using the Pearson Chi-square of the fit.     -   5. Generated area under the Receiver Operating Characteristic         curve (ROC) values. Area under the ROC curve is a measure of         predictive accuracy of subject specific % methylation and was         estimated for the primary analysis (cases vs. disease control)         and the secondary analysis (cases vs. normal colon/buffy coat),         separately.     -   6. In a similar fashion to #5, the fold-change (FC, a measure of         the separation between cases and controls) for the primary and         secondary analysis was also estimated using the ratio of mean %         methylation between cases and corresponding control group.     -   7. 4-6 above was conducted on individual CpG sites as well as         methylated CpG regions. These regions were defined for each         chromosome as a group of at least 5 CpG sites within roughly 100         base pairs (bps) distance with a mean % methylation <2.5% in         normal colon/buffy coat controls     -   8. CpG regions showing promise for technical and biological         validation were identified as having a statistical significant         methylation difference, a large FC, and a high AUC for either         the primary or secondary analyses.

Post-R Analysis:

-   -   1. Sorted individual CpGs and CpG regions by p-value, FC, and         AUC. Cut-offs were <0.01, >20, and >0.85 respectively, although         these were often adjusted depending on the robustness of the         data. For example, highly heterogeneous neoplastic tissue         results in lower % methylation values, which in turn affects the         filtering. Primary and secondary comparisons can be sorted         together or separately depending on the specificity requirements         of the application. Normal colonic epithelia are included as a         control for uncovering markers suitable for stool assay. If         pancreatic juice is being tested, colonic tissue is unnecessary.         This can result in a completely different set of markers.     -   2. Ranked marker regions based on assay platform requirements.         Currently, methylation-specific PCR (MSP), or similar         amplification platforms where discrimination is based on the         specificity of primer annealing, is the platform of choice. For         this methodology, it is imperative to have 2-5 discriminate CpGs         per oligo within an amplifiable stretch of DNA. For stool         assays, this requirement is even more stringent in that         amplicons must be short (<100 bp). Marker selection, therefore,         needs to made on the basis of short contiguous stretches of         highly discriminate CpGs. If the platform evolves to a         sequence-based technology, the CpG distribution requirements         within a region may be entirely different.

Results

Matched, blinded, randomly allocated DNA extracts from 18 pancreatic cancer tumors, 18 benign pancreatic control tissues and 18 normal colonic epithelial tissues were sequenced by RRBS. Median age was 61 (interquartile range 52-65), 61% were women, and 44% were current or former smokers. Roughly 6 million CpGs were mapped at ≥10× coverage. More than 2000 CpG regions met significance criteria for differential methylation. After applying the filter criteria above, 449 differentially methylated regions (DMR) were identified (Table 10). Table 11 presents the identified 449 differentially methylated regions (DMR) ranked by decreasing area under the ROC curve (AUC).

In these markers, methylation signatures range from 3 neighboring CpGs to 56 CpGs. Methylation levels of the pancreatic cancers rarely exceeded 25% at filtered CpGs, which suggested that the cancer tissues may have high levels of contaminating normal cells and/or stroma. To test this, each of the cancers was sequenced for KRAS mutations to verify allele frequencies for the positive samples. For the 50% that harbored a heterozygous KRAS base change, the frequency of the mutant allele was at least 4 times less than the corresponding wild-type allele, in support of contamination by normal cells and/or stroma.

It was found that 58 of the 449 markers are in nonannotated regions and lie in genomic regions without protein coding elements. Of the remaining 391 candidate markers, approximately 225 have been described as associated with cancer, some of which classify as tumor suppressors. The 166 other candidate markers have a previously identified weak association with cancer (e.g., mutations and/or copy number alterations observed in genome-wide screens) or have no previously identified cancer associations.

TABLE 10 DMR Marker # Chromosome Chromosome Coordinates Annotation  1 chr7 87229775-87229856 ABCB1  2 chr2 207307687-207307794 ADAM23  3 chr15 100881373-100881437 ADAMTS17  4 chr16 77468655-77468742 ADAMTS18  5 chr19 41224781-41225006 ADCK4  6 chr7 45613877-45614572 ADCY1  7 chr2 70994498-70994755 ADD2  8 chr14 105190863-105191031 ADSSL1  9 chr10 116064516-116064600 AFAP1L2  10 chr4 87934353-87934488 AFF1  11 chr2 100720494-100720679 AFF3  12 chr7 100136884-100137350 AGFG2  13 chr9 116151083-116151315 ALAD  14 chr14 103396870-103396920 AMN  15 chr19 10206736-10206757 ANGPTL6  16 chr19 17438929-17438974 ANO8  17 chr15 90358267-90358400 ANPEP  18 chr15 29131299-29131369 APBA2  19 chr19 45430362-45430458 APOC1P1  20 chr13 111767862-111768355 ARHGEF7  21 chr7 98990897-98990989 ARPC1B  22 chr22 51066374-51066431 ARSA  23 chr9 120175665-120176057 ASTN2  24 chr1 203619509-203619829 ATP2B4  25 chr7 69062853-69062972 AUTS2  26 chr8 104152963-104152974 BAALC  27 chr11 64052053-64052132 BAD  28 chr10 121411207-121411375 BAG3  29 chr7 98029116-98029383 BAIAP2L1  30 chr9 135462730-135462765 BARHL1  31 chr10 133795124-133795423 BNIP3  32 chr12 107715014-107715095 BTBD11  33 chr6 105584524-105584800 BVES  34 chr10 21816267-21816490 C10orf140  35 chr12 21680381-21680438 C12orf39  36 chr12 21680681-21680817 C12orf39  37 chr12 117174873-117175030 C12orf49  38 chr13 46960767-46961669 C13orf18  39 chr14 50099743-50099930 C14orf104  40 chr19 16772631-16772712 C19orf42  41 chr20 31061389-31061649 C20orf112  42 chr5 175665232-175665311 C5orf25  43 chr6 42858890-42859092 C6orf226  44 chr9 139735581-139735683 C9orf172  45 chr12 2800756-2800899 CACNA1C  46 chr3 54156904-54156987 CACNA2D3  47 chr11 115373179-115373281 CADM1  48 chr16 89007413-89007432 CBFA2T3  49 chr16 49316205-49316258 CBLN1  50 chr21 44495919-44495933 CBS  51 chr17 77810085-77810206 CBX4  52 chr17 8649567-8649665 CCDC42  53 chr11 64110001-64110069 CCDC88B  54 chr14 91883473-91883674 CCDC88C  55 chr14 99946756-99946806 CCNK  56 chr1 158150797-158151205 CD1D  57 chr5 175969660-175969699 CDHR2  58 chr7 39989959-39990020 CDK13  59 chr16 80837397-80837505 CDYL2  60 chr10 11059508-11060151 CELF2  61 chr22 47130339-47130459 CERK  62 chr2 233389020-233389049 CHRND  63 chr7 73245708-73245798 CLDN4  64 chr19 51228217-51228732 CLEC11A  65 chr3 139654045-139654132 CLSTN2  66 chr7 155302557-155302639 CNPY1  67 chr6 88875699-88875763 CNR1  68 chr6 88876367-88876445 CNR1  69 chr6 88876701-88876726 CNR1  70 chr2 165698520-165698578 COBLL1  71 chr6 75794978-75795024 COL12A1  72 chr12 48398051-48398093 COL2A1  73 chr12 48398306-48398375 COL2A1  74 chr18 449695-449798 COLEC12  75 chr7 30721980-30722020 CRHR2  76 chr16 84875643-84875772 CRISPLD2  77 chr7 151127086-151127195 CRYGN  78 chr10 126812450-126812653 CTBP2  79 chr20 56089440-56089547 CTCFL  80 chr2 219261190-219261327 CTDSP1  81 chr2 80530326-80530374 CTNNA2  82 chr22 43044555-43044737 CYB5R3  83 chr19 1406516-1406625 DAZAP1  84 chr7 44084171-44084235 DBNL  85 chr11 20178177-20178304 DBX1  86 chr4 151000325-151000356 DCLK2  87 chr4 151000358-151000403 DCLK2  88 chr4 183817058-183817157 DCTD  89 chr13 52378159-52378202 DHRS12  90 chr8 13014567-13014682 DLC1  91 chr11 84432067-84432186 DLG2  92 chr6 170598276-170598782 DLL1  93 chr19 39989824-39989852 DLL3  94 chr19 12996198-12996321 DNASE2  95 chr2 230578698-230578802 DNER  96 chr2 225907414-225907537 DOCK10  97 chr18 32073971-32074004 DTNA  98 chr2 233352345-233352605 ECEL1  99 chr7 37487539-37488596 ELMO1 100 chr20 39995010-39995051 EMILIN3 101 chr19 48833763-48833967 EMP3 102 chr2 119607676-119607765 EN1 103 chr3 27763358-27763617 EOMES 104 chr3 27763909-27763981 EOMES 105 chr12 132435207-132435428 EP400 106 chr19 16473958-16474095 EPS15L1 107 chr6 152129293-152129450 ESR1 108 chr3 185825887-185826002 ETV5 109 chr9 140201493-140201583 EXD3 110 chr6 133562127-133562229 EYA4 111 chr1 160983607-160983768 F11R 112 chr20 821836-821871 FAM110A 113 chr22 45898798-45898888 FBLN1 114 chr9 97401449-97401602 FBP1 115 chr16 750679-750715 FBXL16 116 chr5 15500208-15500399 FBXL7 117 chr5 15500663-15500852 FBXL7 118 chr5 114880375-114880442 FEM1C 119 chr20 34189488-34189693 FER1L4 120 chr14 53417493-53417618 FERMT2 121 chr2 219849962-219850042 FEV 122 chr17 7339280-7339492 FGF11 123 chr19 49256413-49256451 FGF21 124 chr10 103538848-103539033 FGF8 125 chr11 64008415-64008495 FKBP2 126 chr11 128564106-128564209 FLI1 127 chr10 102985059-102985130 FLJ41350 128 chr13 28674451-28674629 FLT3 129 chr1 240255240-240255264 FMN2 130 chr5 131132146-131132232 FNIP1 131 chr6 108882636-108882682 FOXO3 132 chr3 71478053-71478206 FOXP1 133 chr7 113724864-113725006 FOXP2 134 chr7 113727624-113727693 FOXP2 135 chr5 160975098-160975142 GABRB2 136 chr12 51786085-51786218 GALNT6 137 chr5 179780839-179780955 GFPT2 138 chr20 3641457-3641537 GFRA4 139 chr17 4462834-4463034 GGT6 140 chr17 4463796-4464037 GGT6 141 chr17 42907549-42907807 GJC1 142 chr8 144358251-144358266 GLI4 143 chr16 4377510-4377615 GLIS2 144 chr12 56881329-56881414 GLS2 145 chr6 24776486-24776667 GMNN 146 chr19 3095019-3095055 GNA11 147 chr22 19710910-19710984 GP1BB 148 chr22 19711364-19711385 GP1BB 149 chr2 131485151-131485219 GPR148 150 chr2 165477564-165477609 GRB14 151 chr2 165477839-165477886 GRB14 152 chr17 73390467-73390597 GRB2 153 chr19 48918266-48918311 GRIN2D 154 chr19 48946755-48946912 GRIN2D 155 chr13 114018369-114018421 GRTP1 156 chr12 13254503-13254606 GSG1 157 chr7 43152309-43152375 HECW1 158 chr7 139440133-139440341 HIPK2 159 chr6 34205664-34206018 HMGA1 160 chr12 121416542-121416670 HNF1A 161 chr20 42984244-42984427 HNF4A 162 chr20 43040031-43040119 HNF4A 163 chr5 177632203-177632260 HNRNPAB 164 chr7 27136030-27136245 HOXA1 165 chr2 176971915-176971968 HOXD11 166 chr19 35540057-35540200 HPN 167 chr2 163174366-163174659 IFIH1 168 chr17 47073421-47073440 IGF2BP1 169 chr11 133797643-133797789 IGSF9B 170 chr7 50343838-50344029 IKZF1 171 chr7 50344414-50344453 IKZF1 172 chr20 20345123-20345150 INSM1 173 chr20 20350520-20350532 INSM1 174 chr15 76632356-76632462 ISL2 175 chr2 182321880-182322022 ITGA4 176 chr2 182322168-182322198 ITGA4 177 chr2 173293542-173293644 ITGA6 178 chr19 2097386-2097437 IZUMO4 179 chr21 27011846-27011964 JAM2 180 chr2 47797260-47797371 KCNK12 181 chr10 79397895-79397945 KCNMA1 182 chr5 113696524-113696682 KCNN2 183 chr5 113696971-113697058 KCNN2 184 chr1 154733071-154733232 KCNN3 185 chr8 99439457-99439482 KCNS2 186 chr19 34287890-34287972 KCTD15 187 chr12 121905558-121905792 KDM2B 188 chr8 136469529-136469873 KHDRBS3 189 chr16 85646495-85646594 KIAA0182 190 chr18 46190841-46190970 KIAA0427 191 chr4 37245694-37245718 KIAA1239 192 chr17 72350351-72350403 KIF19 193 chr2 149633039-149633137 KIF5C 194 chr22 50987245-50987312 KLHDC7B 195 chr12 53298237-53298384 KRT8 196 chr19 54974004-54974086 LENG9 197 chr1 180198528-180198542 LHX4 198 chr19 2290471-2290541 LINGO3 199 chr11 19733958-19734013 LOC100126784 200 chr19 58513829-58513851 LOC100128398 201 chr17 43324999-43325188 LOC100133991 202 chr17 43325784-43325960 LOC100133991 203 chr2 109745715-109745742 LOC100287216 204 chr1 178063099-178063167 LOC100302401 205 chr12 53447992-53448072 LOC283335 206 chr1 45769962-45770141 LOC400752 207 chr20 61637950-61638000 LOC63930 208 chr13 88323571-88323647 LOC642345 209 chr6 111873064-111873162 LOC643749 210 chr5 87956937-87956996 LOC645323 211 chr5 87970260-87970568 LOC645323 212 chr5 87970751-87970850 LOC645323 213 chr12 85430135-85430175 LRRIQ1 214 chr19 497878-497933 MADCAM1 215 chr5 71404528-71404563 MAP1B 216 chr2 39665069-39665282 MAP4K3 217 chr1 156406057-156406118 MAX.chr1.156406057-156406118 218 chr1 23894874-23894919 MAX.chr1.23894874-23894919 219 chr1 240161479-240161546 MAX.chr1.240161479-240161546 220 chr1 244012804-244012986 MAX.chr1.244012804-244012986 221 chr1 35394690-35394876 MAX.chr1.35394690-35394876 222 chr1 35395179-35395201 MAX.chr1.35395179-35395201 223 chr1 39044345-39044354 MAX.chr1.39044345-39044354 224 chr10 101282185-101282257 MAX.chr10.101282185- 101282257 225 chr10 127033272-127033428 MAX.chr10.127033272- 127033428 226 chr11 120382450-120382498 MAX.chr11.120382450- 120382498 227 chr11 47421719-47421776 MAX.chr11.47421719-47421776 228 chr12 133484978-133485066 MAX.chr12.133484978- 133485066 229 chr12 133485702-133485739 MAX.chr12.133485702- 133485739 230 chr12 54151078-54151153 MAX.chr12.54151078-54151153 231 chr12 58259413-58259475 MAX.chr12.58259413-58259475 232 chr13 25322044-25322165 MAX.chr13.25322044-25322165 233 chr13 29394692-29394771 MAX.chr13.29394692-29394771 234 chr14 100751586-100751695 MAX.chr14.100751586- 100751695 235 chr14 61123624-61123707 MAX.chr14.61123624-61123707 236 chr14 89507100-89507162 MAX.chr14.89507100-89507162 237 chr15 40361431-40361644 MAX.chr15.40361431-40361644 238 chr15 89942904-89943197 MAX.chr15.89942904-89943197 239 chr16 25042924-25043187 MAX.chr16.25042924-25043187 240 chr16 85230248-85230405 MAX.chr16.85230248-85230405 241 chr17 1835463-1835690 MAX.chr17.1835463-1835690 242 chr17 60218266-60218449 MAX.chr17.60218266-60218449 243 chr17 76337726-76337824 MAX.chr17.76337726-76337824 244 chr19 11805543-11805639 MAX.chr19.11805543-11805639 245 chr19 22034747-22034887 MAX.chr19.22034747-22034887 246 chr19 32715650-32715707 MAX.chr19.32715650-32715707 247 chr19 5805881-5805968 MAX.chr19.5805881-5805968 248 chr2 127783183-127783233 MAX.chr2.127783183-127783233 249 chr2 232530964-232531124 MAX.chr2.232530964-232531124 250 chr2 239957125-239957163 MAX.chr2.239957125-239957163 251 chr2 43153331-43153424 MAX.chr2.43153331-43153424 252 chr2 71503632-71503860 MAX.chr2.71503632-71503860 253 chr20 43948422-43948484 MAX.chr20.43948422-43948484 254 chr21 47063798-47063877 MAX.chr21.47063798-47063877 255 chr22 17849540-17849622 MAX.chr22.17849540-17849622 256 chr22 38732124-38732211 MAX.chr22.38732124-38732211 257 chr22 42764974-42765049 MAX.chr22.42764974-42765049 258 chr22 46974925-46975007 MAX.chr22.46974925-46975007 259 chr22 50342922-50343232 MAX.chr22.50342922-50343232 260 chr3 132273353-132273532 MAX.chr3.132273353-132273532 261 chr3 193858771-193858843 MAX.chr3.193858771-193858843 262 chr3 24563009-24563117 MAX.chr3.24563009-24563117 263 chr3 75411368-75411473 MAX.chr3.75411368-75411473 264 chr4 26828422-26828522 MAX.chr4.26828422-26828522 265 chr4 8965831-8965868 MAX.chr4.8965831-8965868 266 chr5 142100518-142100780 MAX.chr5.142100518-142100780 267 chr6 169613138-169613249 MAX.chr6.169613138-169613249 268 chr6 64168133-64168268 MAX.chr6.64168133-64168268 269 chr7 129794337-129794536 MAX.chr7.129794337-129794536 270 chr7 1705957-1706065 MAX.chr7.1705957-1706065 271 chr7 28893550-28893569 MAX.chr7.28893550-28893569 272 chr7 47650711-47650882 MAX.chr7.47650711-47650882 273 chr7 64408106-64408135 MAX.chr7.64408106-64408135 274 chr9 108418404-108418453 MAX.chr9.108418404-108418453 275 chr9 120507310-120507354 MAX.chr9.120507310-120507354 276 chr5 89769002-89769411 MBLAC2 277 chr12 51319165-51319319 METTL7A 278 chr2 191272534-191272765 MFSD6 279 chr19 6236947-6237089 MLLT1 280 chr6 168333306-168333467 MLLT4 281 chr8 89339567-89339662 MMP16 282 chr17 2300399-2300476 MNT 283 chr7 156802460-156802490 MNX1 284 chr19 4343896-4242968 MPND 285 chr16 56715756-56716025 MT1X 286 chr15 48470062-48470503 MYEF2 287 chr15 48470606-48470725 MYEF2 288 chr5 16936010-16936058 MYO10 289 chr3 39851068-39851989 MYRIP 290 chr13 33001061-33001251 N4BP2L1 291 chr4 2060477-2060624 NAT8L 292 chr12 125002129-125002192 NCOR2 293 chr16 23607524-23607650 NDUFAB1 294 chr10 105338596-105338843 NEURL 295 chr1 204797773-204797785 NFASC 296 chr2 233877877-233878027 NGEF 297 chr18 31803017-31803114 NOL4 298 chr9 139438534-139438629 NOTCH1 299 chr5 32714270-32714325 NPR3 300 chr9 127266951-127267032 NR5A1 301 chr11 124615979-124616029 NRGN 302 chr11 124616860-124617005 NRGN 303 chr20 327754-327871 NRSN2 304 chr8 99952501-99952533 OSR2 305 chr5 76926598-76926703 OTP 306 chr3 8809858-8809865 OXTR 307 chr19 14172823-14172948 PALM3 308 chr6 52268531-52268702 PAQR8 309 chr20 21686466-21686563 PAX1 310 chr21 47063793-47064177 PCBP3 311 chr7 100203461-100203600 PCOLCE 312 chr4 657555-657666 PDE6B 313 chr7 544848-545022 PDGFA 314 chr2 239194812-239194946 PER2 315 chr19 43979400-43979435 PHLDB3 316 chr6 144384503-144385539 PLAGL1 317 chr2 28844174-28844270 PLB1 318 chr1 242687719-242687746 PLD5 319 chr12 6419210-6419489 PLEKHG6 320 chr22 50745629-50745727 PLXNB2 321 chr2 105471752-105471787 POU3F3 322 chr13 79177868-79177951 POU4F1 323 chr1 203044913-203044929 PPFIA4 324 chr22 50825886-50825981 PPP6R2 325 chr17 74519328-74519457 PRCD 326 chr7 601162-601552 PRKAR1B 327 chr16 23846964-23847339 PRKCB 328 chr16 23847507-23847617 PRKCB 329 chr16 23847825-23848168 PRKCB 330 chr22 18923785-18923823 PRODH 331 chr22 45099093-45099304 PRR5 332 chr3 9988302-9988499 PRRT3 333 chr1 11538685-11538738 PTCHD2 334 chr1 11539396-11539540 PTCHD2 335 chr10 23480864-23480913 PTF1A 336 chr19 5340273-5340743 PTPRS 337 chr2 1747034-1747126 PXDN 338 chr2 1748338-1748444 PXDN 339 chr7 4923056-4923107 RADIL 340 chr19 15568448-15568639 RASAL3 341 chr5 80256215-80256313 RASGRF2 342 chr17 77179784-77179887 RBFOX3 343 chr4 40516823-40516984 RBM47 344 chr4 57775698-57775771 REST 345 chr10 43572798-43572896 RET 346 chr10 121302439-121302501 RGS10 347 chr16 318717-318893 RGS11 348 chr1 241520322-241520334 RGS7 349 chr1 42846119-42846174 RIMKLA 350 chr21 43189031-43189229 RIPK4 351 chr7 5821188-5821283 RNF216 352 chr19 23941063-23941142 RPSAP58 353 chr19 23941384-23941670 RPSAP58 354 chr16 29118636-29118891 RRN3P2 355 chr6 127440492-127441039 RSPO3 356 chr17 42392669-42392701 RUNDC3A 357 chr6 45345446-45345595 RUNX2 358 chr6 45387405-45387456 RUNX2 359 chr3 72496092-72496361 RYBP 360 chr22 20785373-20785464 SCARF2 361 chr8 145561664-145561696 SCRT1 362 chr7 54826636-54826706 SEC61G 363 chr10 38691448-38691521 SEPT7L 364 chr4 154712157-154712232 SFRP2 365 chr7 155597793-155597973 SHH 366 chr4 77610781-77610824 SHROOM3 367 chr21 38120336-38120558 SIM2 368 chr15 68115602-68115675 SKOR1 369 chr17 6949717-6949778 SLC16A11 370 chr11 35441199-35441260 SLC1A2 371 chr19 59025337-59025385 SLC27A5 372 chr2 27486089-27486170 SLC30A3 373 chr12 69140018-69140206 SLC35E3 374 chr12 46661132-46661306 SLC38A1 375 chr3 50243467-50243553 SLC38A3 376 chr7 150760388-150760530 SLC4A2 377 chr5 1445384-1445473 SLC6A3 378 chr2 40679298-40679326 SLC8A1 379 chr5 506178-506343 SLC9A3 380 chr20 61284095-61284194 SLCO4A1 381 chr5 101631546-101631731 SLCO4C1 382 chr10 98945242-98945493 SLIT1 383 chr13 88330094-88330355 SLITRK5 384 chr15 66999854-67000014 SMAD6 385 chr10 112064230-112064280 SMNDC1 386 chr6 84419007-84419072 SNAP91 387 chr17 36508733-36508891 SOCS7 388 chr4 7367687-7367825 SORCS2 389 chr17 70116754-70116823 SOX9 390 chr4 57687746-57687764 SPINK2 391 chr3 140770014-140770193 SPSB4 392 chr17 36762706-36762763 SRCIN1 393 chr6 43141954-43142058 SRF 394 chr7 105029460-105029585 SRPK2 395 chr16 70415312-70415673 ST3GAL2 396 chr2 107502978-107503055 ST6GAL2 397 chr2 107503155-107503391 ST6GAL2 398 chr12 22487528-22487848 ST8SIA1 399 chr10 17496177-17496310 ST8SIA6 400 chr2 242447608-242447724 STK25 401 chr3 120626999-120627116 STXBP5L 402 chr3 33260338-33260423 SUSD5 403 chr16 19179713-19179744 SYT17 404 chr12 115122614-115122632 TBX3 405 chr19 3606372-3606418 TBXA2R 406 chr10 70359250-70359439 TET1 407 chr16 4310204-4310233 TFAP4 408 chr21 32930371-32930409 TIAM1 409 chr4 942190-942382 TMEM175 410 chr6 130686773-130686820 TMEM200a 411 chr6 130687200-130687735 TMEM200a 412 chr3 185215700-185215782 TMEM41A 413 chr20 42544780-42544835 TOX2 414 chr9 140091343-140091644 TPRN 415 chr8 126441476-126441519 TRIB1 416 chr5 14143759-14143880 TRIO 417 chr22 38148620-38148716 TRIOBP 418 chr7 19156788-19157227 TWIST1 419 chr7 19157436-19157533 TWIST1 420 chr4 41259387-41259594 UCHL1 421 chr15 63795401-63795636 USP3 422 chr17 9548120-9548325 USP43 423 chr12 95942077-95942558 USP44 424 chr10 17271896-17271994 VIM 425 chr7 49813135-49814168 VWC2 426 chr7 151078646-151078674 WDR86 427 chr12 49372205-49372274 WNT1 428 chr11 32460759-32460800 WT1 429 chr19 4061206-4061360 ZBTB7A 430 chr8 144623045-144623088 ZC3H3 431 chr2 145274698-145274874 ZEB2 432 chr19 38146299-38146397 ZFP30 433 chr16 88521287-88521377 ZFPM1 434 chr4 2298384-2298498 ZFYVE28 435 chr4 2415252-2415286 ZFYVE28 436 chr20 45986341-45986684 ZMYND8 437 chr22 22862957-22862983 ZNF280B 438 chr6 43336449-43336545 ZNF318 439 chr19 53661819-53662279 ZNF347 440 chr16 88497041-88497148 ZNF469 441 chr19 57019064-57019137 ZNF471 442 chr19 2842178-2842235 ZNF555 443 chr19 37958078-37958134 ZNF570 444 chr8 125985552-125985847 ZNF572 445 chr19 53696101-53696195 ZNF665 446 chr19 53696497-53696704 ZNF665 447 chr19 20149796-20149923 ZNF682 448 chr19 571 06617-571 06967 ZNF71 449 chr7 6655380-6655652 ZNF853 In Table 10, bases are numbered according to the February 2009 human genome assembly GRCh37/hg19 (see, e.g., Rosenbloom et al. (2012) “ENCODE whole-genome data in the UCSC Genome Browser: update 2012” Nucleic Acids Research 40: D912-D917). The marker names BHLHE23 and LOC63930 refer to the same marker.

TABLE 11 Chromosome Area under Chromosome Coordinates Annotation the ROC Curve chr12 53298237-53298384 KRT8 1.00 chr7 129794337-129794536 MAX.chr7.129794337-129794536 1.00 chr10 101282185-101282257 MAX.chr10.101282185- 101282257 0.99 chr10 126812450-126812653 CTBP2 0.99 chr9 116151083-116151315 ALAD 0.99 chr8 13014567-13014682 DLC1 0.99 chr7 139440133-139440341 HIPK2 0.99 chr3 39851068-39851989 MYRIP 0.99 chr19 4061206-4061360 ZBTB7A 0.99 chr16 84875643-84875772 CRISPLD2 0.99 chr6 52268531-52268702 PAQR8 0.99 chr2 239194812-239194946 PER2 0.99 chr17 1835463-1835690 MAX.chr17.1835463-1835690 0.99 chr5 506178-506343 SLC9A3 0.99 chr20 31061389-31061649 C20orf112 0.98 chr9 139438534-139438629 NOTCH1 0.98 chr15 48470606-48470725 MYEF2 0.98 chr12 125002129-125002192 NCOR2 0.98 chr4 7367687-7367825 SORCS2 0.98 chr19 6236947-6237089 MLLT1 0.98 chr7 544848-545022 PDGFA 0.98 chr7 98029116-98029383 BAIAP2L1 0.98 chr4 2415252-2415286 ZFYVE28 0.98 chr12 6419210-6419489 PLEKHG6 0.98 chr22 50825886-50825981 PPP6R2 0.97 chr20 45986341-45986684 ZMYND8 0.97 chr5 142100518-142100780 MAX.chr5.142100518-142100780 0.97 chr19 16473958-16474095 EPS15L1 0.97 chr16 29118636-29118891 RRN3P2 0.97 chr6 75794978-75795024 COL12A1 0.97 chr9 139735581-139735683 C9orf172 0.97 chr17 4462834-4463034 GGT6 0.97 chr17 4463796-4464037 GGT6 0.96 chr12 95942077-95942558 USP44 0.96 chr20 42984244-42984427 HNF4A 0.96 chr7 47650711-47650882 MAX.chr7.47650711-47650882 0.96 chr4 942190-942382 TMEM175 0.96 chr7 73245708-73245798 CLDN4 0.96 chr22 46974925-46975007 MAX.chr22.46974925-46975007 0.96 chr10 127033272-127033428 MAX.chr10.127033272- 127033428 0.96 chr3 132273353-132273532 MAX.chr3.132273353-132273532 0.96 chr4 26828422-26828522 MAX.chr4.26828422-26828522 0.96 chr20 61284095-61284194 SLCO4A1 0.96 chr19 35540057-35540200 HPN 0.96 chr22 45099093-45099304 PRR5 0.95 chr17 60218266-60218449 MAX.chr17.60218266-60218449 0.95 chr6 168333306-168333467 MLLT4 0.95 chr10 105338596-105338843 NEURL 0.95 chr9 120175665-120176057 ASTN2 0.95 chr4 183817058-183817157 DCTD 0.95 chr6 108882636-108882682 FOXO3 0.95 chr7 27136030-27136245 HOXA1 0.95 chr19 14172823-14172948 PALM3 0.95 chr3 75411368-75411473 MAX.chr3.75411368-75411473 0.94 chr6 64168133-64168268 MAX.chr6.64168133-64168268 0.94 chr16 318717-318893 RGS11 0.94 chr20 43040031-43040119 HNF4A 0.94 chr7 49813135-49814168 VWC2 0.94 chr16 85230248-85230405 MAX.chr16.85230248-85230405 0.94 chr22 38148620-38148716 TRIOBP 0.94 chr5 89769002-89769411 MBLAC2 0.94 chr1 158150797-158151205 CD1D 0.93 chr19 1406516-1406625 DAZAP1 0.93 chr12 121416542-121416670 HNF1A 0.93 chr17 76337726-76337824 MAX.chr17.76337726-76337824 0.93 chr13 88330094-88330355 SLITRK5 0.93 chr19 54974004-54974086 LENG9 0.93 chr22 47130339-47130459 CERK 0.92 chr7 601162-601552 PRKAR1B 0.92 chr2 70994498-70994755 ADD2 0.92 chr15 40361431-40361644 MAX.chr15.40361431-40361644 0.92 chr19 15568448-15568639 RASAL3 0.92 chr6 24776486-24776667 GMNN 0.92 chr18 449695-449798 COLEC12 0.92 chr7 150760388-150760530 SLC4A2 0.92 chr21 38120336-38120558 SIM2 0.91 chr15 66999854-67000014 SMAD6 0.91 chr2 28844174-28844270 PLB1 0.91 chr11 115373179-115373281 CADM1 0.91 chr21 47063793-47064177 PCBP3 0.91 chr2 1748338-1748444 PXDN 0.91 chr21 47063798-47063877 MAX.chr21.47063798-47063877 0.91 chr16 56715756-56716025 MT1X 0.90 chr4 87934353-87934488 AFF1 0.90 chr9 140091343-140091644 TPRN 0.90 chr5 15500208-15500399 FBXL7 0.90 chr19 48833763-48833967 EMP3 0.90 chr6 43141954-43142058 SRF 0.90 chr3 185215700-185215782 TMEM41A 0.90 chr1 160983607-160983768 F11R 0.90 chr12 58259413-58259475 MAX.chr12.58259413-58259475 0.90 chr2 47797260-47797371 KCNK12 0.89 chr16 4377510-4377615 GLIS2 0.89 chr15 63795401-63795636 USP3 0.89 chr13 33001061-33001251 N4BP2L1 0.89 chr3 120626999-120627116 STXBP5L 0.89 chr7 19156788-19157227 TWIST1 0.89 chr18 46190841-46190970 KIAA0427 0.89 chr7 100203461-100203600 PCOLCE 0.88 chr19 51228217-51228732 CLEC11A 0.88 chr19 17438929-17438974 ANO8 0.88 chr12 2800756-2800899 CACNA1C 0.88 chr6 34205664-34206018 HMGA1 0.88 chr15 76632356-76632462 ISL2 0.88 chr6 111873064-111873162 LOC643749 0.88 chr10 70359250-70359439 TET1 0.88 chr2 39665069-39665282 MAP4K3 0.88 chr2 43153331-43153424 MAX.chr2.43153331-43153424 0.87 chr22 17849540-17849622 MAX.chr22.17849540-17849622 0.87 chr2 233877877-233878027 NGEF 0.87 chr8 89339567-89339662 MMP16 0.87 chr13 46960767-46961669 C13orf18 0.87 chr6 170598276-170598782 DLL1 0.87 chr4 40516823-40516984 RBM47 0.87 chr3 139654045-139654132 CLSTN2 0.87 chr2 27486089-27486170 SLC30A3 0.87 chr17 74519328-74519457 PROD 0.86 chr2 163174366-163174659 IFIH1 0.86 chr4 41259387-41259594 UCHL1 0.86 chr7 45613877-45614572 ADCY1 0.86 chr7 98990897-98990989 ARPC1B 0.86 chr3 54156904-54156987 CACNA2D3 0.86 chr16 49316205-49316258 CBLN1 0.86 chr3 71478053-71478206 FOXP1 0.86 chr5 87956937-87956996 LOC645323 0.86 chr21 43189031-43189229 RIPK4 0.86 chr12 22487528-22487848 ST8SIA1 0.86 chr20 42544780-42544835 TOX2 0.86 chr20 821836-821871 FAM110A 0.86 chr16 4310204-4310233 TFAP4 0.86 chr11 64110001-64110069 CCDC88B 0.85 chr8 136469529-136469873 KHDRBS3 0.85 chr10 102985059-102985130 FLJ41350 0.85 chr2 176971915-176971968 HOXD11 0.85 chr12 51319165-51319319 METTL7A 0.85 chr22 50342922-50343232 MAX.chr22.50342922-50343232 0.85 chr7 155597793-155597973 SHH 0.85 chr4 154712157-154712232 SFRP2 0.84 chr19 57019064-57019137 ZNF471 0.84 chr5 87970260-87970568 LOC645323 0.84 chr6 130686773-130686820 TMEM200a 0.84 chr9 140201493-140201583 EXD3 0.84 chr12 53447992-53448072 LOC283335 0.84 chr22 43044555-43044737 CYB5R3 0.84 chr19 49256413-49256451 FGF21 0.84 chr17 77810085-77810206 CBX4 0.84 chr7 156802460-156802490 MNX1 0.84 chr7 151127086-151127195 CRYGN 0.83 chr6 169613138-169613249 MAX.chr6.169613138-169613249 0.83 chr2 71503632-71503860 MAX.chr2.71503632-71503860 0.83 chr20 21686466-21686563 PAX1 0.83 chr2 173293542-173293644 ITGA6 0.83 chr7 87229775-87229856 ABCB1 0.83 chr2 207307687-207307794 ADAM23 0.83 chr12 21680381-21680438 C12orf39 0.83 chr15 89942904-89943197 MAX.chr15.89942904-89943197 0.83 chr10 43572798-43572896 RET 0.83 chr19 5805881-5805968 MAX.chr19.5805881-5805968 0.83 chr19 53661819-53662279 ZNF347 0.83 chr22 38732124-38732211 MAX.chr22.38732124-38732211 0.83 chr11 124615979-124616029 NRGN 0.83 chr2 100720494-100720679 AFF3 0.83 chr19 497878-497933 MADCAM1 0.82 chr5 14143759-14143880 TRIO 0.82 chr18 32073971-32074004 DTNA 0.82 chr15 48470062-48470503 MYEF2 0.82 chr3 50243467-50243553 SLC38A3 0.82 chr16 70415312-70415673 ST3GAL2 0.82 chr11 35441199-35441260 SLC1A2 0.82 chr12 51786085-51786218 GALNT6 0.82 chr2 232530964-232531124 MAX.chr2.232530964-232531124 0.81 chr22 19710910-19710984 GP1BB 0.81 chr19 2097386-2097437 IZUMO4 0.81 chr11 20178177-20178304 DBX1 0.81 chr7 37487539-37488596 ELMO1 0.81 chr11 128564106-128564209 FLI1 0.81 chr7 105029460-105029585 SRPK2 0.81 chr10 103538848-103539033 FGF8 0.81 chr11 124616860-124617005 NRGN 0.81 chr19 57106617-57106967 ZNF71 0.81 chr9 97401449-97401602 FBP1 0.81 chr5 113696971-113697058 KCNN2 0.80 chr19 53696497-53696704 ZNF665 0.80 chr1 45769962-45770141 LOC400752 0.80 chr14 91883473-91883674 CCDC88C 0.80 chr17 43324999-43325188 LOC100133991 0.80 chr16 23846964-23847339 PRKCB 0.80 chr19 11805543-11805639 MAX.chr19.11805543-11805639 0.80 chr12 117174873-117175030 C12orf49 0.80 chr20 39995010-39995051 EMILIN3 0.80 chr5 87970751-87970850 LOC645323 0.80 chr7 4923056-4923107 RADIL 0.80 chr19 23941063-23941142 RPSAP58 0.80 chr6 45387405-45387456 RUNX2 0.80 chr17 6949717-6949778 SLC16A11 0.80 chr2 165477564-165477609 GRB14 0.80 chr20 34189488-34189693 FER1L4 0.80 chr22 50745629-50745727 PLXNB2 0.79 chr7 155302557-155302639 CNPY1 0.79 chr7 19157436-19157533 TWIST1 0.79 chr1 203619509-203619829 ATP2B4 0.79 chr2 230578698-230578802 DNER 0.79 chr19 23941384-23941670 RPSAP58 0.79 chr17 73390467-73390597 GRB2 0.79 chr15 68115602-68115675 SKOR1 0.79 chr17 2300399-2300476 MNT 0.79 chr13 79177868-79177951 POU4F1 0.79 chr19 59025337-59025385 SLC27A5 0.79 chr9 135462730-135462765 BARHL1 0.78 chr8 125985552-125985847 ZNF572 0.78 chr5 175665232-175665311 C5orf25 0.78 chr6 42858890-42859092 C6orf226 0.78 chr12 21680681-21680817 C12orf39 0.78 chr14 50099743-50099930 C14orf104 0.78 chr5 175969660-175969699 CDHR2 0.78 chr16 80837397-80837505 CDYL2 0.78 chr19 12996198-12996321 DNASE2 0.78 chr13 28674451-28674629 FLT3 0.78 chr1 154733071-154733232 KCNN3 0.78 chr1 35395179-35395201 MAX.chr1.35395179-35395201 0.78 chr19 5340273-5340743 PTPRS 0.78 chr3 33260338-33260423 SUSD5 0.78 chr2 145274698-145274874 ZEB2 0.78 chr13 25322044-25322165 MAX.chr13.25322044-25322165 0.78 chr2 80530326-80530374 CTNNA2 0.78 chr12 56881329-56881414 GLS2 0.78 chr3 24563009-24563117 MAX.chr3.24563009-24563117 0.78 chr7 6655380-6655652 ZNF853 0.78 chr4 2298384-2298498 ZFYVE28 0.77 chr5 177632203-177632260 HNRNPAB 0.77 chr22 19711364-19711385 GP1BB 0.77 chr2 165477839-165477886 GRB14 0.77 chr13 29394692-29394771 MAX.chr13.29394692-29394771 0.77 chr14 103396870-103396920 AMN 0.77 chr12 132435207-132435428 EP400 0.77 chr8 99439457-99439482 KCNS2 0.77 chr7 5821188-5821283 RNF216 0.77 chr17 9548120-9548325 USP43 0.77 chr3 185825887-185826002 ETV5 0.77 chr12 121905558-121905792 KDM2B 0.77 chr3 193858771-193858843 MAX.chr3.193858771-193858843 0.77 chr19 53696101-53696195 ZNF665 0.77 chr7 69062853-69062972 AUTS2 0.77 chr1 242687719-242687746 PLD5 0.76 chr20 43948422-43948484 MAX.chr20.43948422-43948484 0.76 chr6 84419007-84419072 SNAP91 0.76 chr17 43325784-43325960 LOC100133991 0.76 chr19 41224781-41225006 ADCK4 0.76 chr5 15500663-15500852 FBXL7 0.76 chr20 20350520-20350532 INSM1 0.76 chr1 23894874-23894919 MAX.chr1.23894874-23894919 0.76 chr1 11538685-11538738 PTCHD2 0.76 chr14 105190863-105191031 ADSSL1 0.76 chr22 22862957-22862983 ZNF280B 0.76 chr17 72350351-72350403 KIF19 0.76 chr7 50343838-50344029 IKZF1 0.76 chr2 191272534-191272765 MFSD6 0.76 chr17 47073421-47073440 IGF2BP1 0.76 chr10 133795124-133795423 BNIP3 0.75 chr5 101631546-101631731 SLCO4C1 0.75 chr12 133485702-133485739 MAX.chr12.133485702- 133485739 0.75 chr22 18923785-18923823 PRODH 0.75 chr20 56089440-56089547 CTCFL 0.75 chr6 43336449-43336545 ZNF318 0.75 chr14 61123624-61123707 MAX.chr14.61123624-61123707 0.75 chr7 30721980-30722020 CRHR2 0.75 chr17 7339280-7339492 FGF11 0.75 chr11 84432067-84432186 DLG2 0.75 chr2 233352345-233352605 ECEL1 0.75 chr3 27763358-27763617 EOMES 0.75 chr5 160975098-160975142 GABRB2 0.75 chr1 244012804-244012986 MAX.chr1.244012804-244012986 0.75 chr16 25042924-25043187 MAX.chr16.25042924-25043187 0.75 chr4 57775698-57775771 REST 0.75 chr6 127440492-127441039 RSPO3 0.75 chr8 145561664-145561696 SCRT1 0.75 chr8 144623045-144623088 ZC3H3 0.75 chr12 48398051-48398093 COL2A1 0.75 chr2 182321880-182322022 ITGA4 0.75 chr9 120507310-120507354 MAX.chr9.120507310-120507354 0.74 chr6 133562127-133562229 EYA4 0.74 chr2 127783183-127783233 MAX.chr2.127783183-127783233 0.74 chr11 47421719-47421776 MAX.chr11.47421719-47421776 0.74 chr19 10206736-10206757 ANGPTL6 0.74 chr2 225907414-225907537 DOCK10 0.74 chr1 35394690-35394876 MAX.chr1.35394690-35394876 0.74 chr4 2060477-2060624 NAT8L 0.74 chr2 1747034-1747126 PXDN 0.74 chr6 45345446-45345595 RUNX2 0.74 chr7 50344414-50344453 IKZF1 0.74 chr1 180198528-180198542 LHX4 0.74 chr14 53417493-53417618 FERMT2 0.74 chr17 77179784-77179887 RBFOX3 0.74 chr10 98945242-98945493 SLIT1 0.74 chr2 40679298-40679326 SLC8A1 0.74 chr12 48398306-48398375 COL2A1 0.74 chr22 50987245-50987312 KLHDC7B 0.73 chr12 54151078-54151153 MAX.chr12.54151078-54151153 0.73 chr7 28893550-28893569 MAX.chr7.28893550-28893569 0.73 chr10 38691448-38691521 SEPT7L 0.73 chr1 203044913-203044929 PPFIA4 0.73 chr22 51066374-51066431 ARSA 0.73 chr7 113724864-113725006 FOXP2 0.73 chr12 13254503-13254606 GSG1 0.73 chr11 19733958-19734013 LOC100126784 0.73 chr1 39044345-39044354 MAX.chr1.39044345-39044354 0.73 chr3 9988302-9988499 PRRT3 0.73 chr22 20785373-20785464 SCARF2 0.73 chr6 130687200-130687735 TMEM200a 0.73 chr12 46661132-46661306 SLC38A1 0.73 chr19 20149796-20149923 ZNF682 0.73 chr11 133797643-133797789 IGSF9B 0.73 chr2 105471752-105471787 POU3F3 0.72 chr5 179780839-179780955 GFPT2 0.72 chr8 99952501-99952533 OSR2 0.72 chr19 16772631-16772712 C19orf42 0.72 chr2 119607676-119607765 EN1 0.72 chr12 49372205-49372274 WNT1 0.72 chr5 113696524-113696682 KCNN2 0.72 chr17 8649567-8649665 CCDC42 0.72 chr7 1705957-1706065 MAX.chr7.1705957-1706065 0.71 chr2 149633039-149633137 KIF5C 0.71 chr19 2842178-2842235 ZNF555 0.71 chr10 121302439-121302501 RGS10 0.71 chr21 44495919-44495933 CBS 0.71 chr10 11059508-11060151 CELF2 0.71 chr19 48946755-48946912 GRIN2D 0.71 chr12 133484978-133485066 MAX.chr12.133484978- 133485066 0.71 chr5 16936010-16936058 MYO10 0.71 chr17 42392669-42392701 RUNDC3A 0.71 chr16 88521287-88521377 ZFPM1 0.71 chr4 37245694-37245718 KIAA1239 0.71 chr16 23847507-23847617 PRKCB 0.71 chr5 76926598-76926703 OTP 0.71 chr18 31803017-31803114 NOL4 0.71 chr2 182322168-182322198 ITGA4 0.70 chr15 90358267-90358400 ANPEP 0.70 chr12 107715014-107715095 BTBD11 0.70 chr16 89007413-89007432 CBFA2T3 0.70 chr4 151000325-151000356 DCLK2 0.70 chr6 152129293-152129450 ESR1 0.70 chr19 38146299-38146397 ZFP30 0.70 chr1 204797773-204797785 NFASC 0.70 chr22 42764974-42765049 MAX.chr22.42764974-42765049 0.70 chr2 165698520-165698578 COBLL1 0.70 chr8 144358251-144358266 GLI4 0.70 chr2 219261190-219261327 CTDSP1 0.70 chr2 239957125-239957163 MAX.chr2.239957125-239957163 0.70 chr10 121411207-121411375 BAG3 0.69 chr2 233389020-233389049 CHRND 0.69 chr14 99946756-99946806 CONK 0.69 chr11 120382450-120382498 MAX.chr11.120382450- 0.69 120382498 chr16 750679-750715 FBXL16 0.69 chr15 100881373-100881437 ADAMTS17 0.69 chr1 11539396-11539540 PTCHD2 0.69 chr2 242447608-242447724 STK25 0.69 chr16 23847825-23848168 PRKCB 0.69 chr17 42907549-42907807 GJC1 0.69 chr19 48918266-48918311 GRIN2D 0.69 chr10 79397895-79397945 KCNMA1 0.69 chr5 71404528-71404563 MAP1B 0.69 chr19 43979400-43979435 PHLDB3 0.69 chr17 70116754-70116823 SOX9 0.69 chr16 88497041-88497148 ZNF469 0.69 chr2 131485151-131485219 GPR148 0.69 chr8 126441476-126441519 TRIB1 0.68 chr4 151000358-151000403 DCLK2 0.68 chr19 39989824-39989852 DLL3 0.68 chr14 89507100-89507162 MAX.chr14.89507100-89507162 0.68 chr12 115122614-115122632 TBX3 0.68 chr19 58513829-58513851 LOC100128398 0.68 chr5 32714270-32714325 NPR3 0.68 chr3 140770014-140770193 SPSB4 0.68 chr6 88875699-88875763 CNR1 0.68 chr4 657555-657666 PDE6B 0.68 chr16 19179713-19179744 SYT17 0.67 chr3 8809858-8809865 OXTR 0.67 chr10 116064516-116064600 AFAP1L2 0.67 chr4 77610781-77610824 SHROOM3 0.67 chr6 88876367-88876445 CNR1 0.67 chr7 151078646-151078674 WDR86 0.67 chr2 109745715-109745742 LOC100287216 0.67 chr14 100751586-100751695 MAX.chr14.100751586- 0.67 100751695 chr21 32930371-32930409 TIAM1 0.67 chr4 57687746-57687764 SPINK2 0.67 chr2 219849962-219850042 FEV 0.66 chr20 327754-327871 NRSN2 0.66 chr1 178063099-178063167 LOC100302401 0.66 chr19 45430362-45430458 APOC1P1 0.66 chr13 111767862-111768355 ARHGEF7 0.66 chr19 37958078-37958134 ZNF570 0.66 chr19 32715650-32715707 MAX.chr19.32715650-32715707 0.66 chr8 104152963-104152974 BAALC 0.66 chr19 3095019-3095055 GNA11 0.66 chr19 3606372-3606418 TBXA2R 0.66 chr12 69140018-69140206 SLC35E3 0.66 chr4 8965831-8965868 MAX.chr4.8965831-8965868 0.66 chr17 36508733-36508891 SOCS7 0.66 chr16 85646495-85646594 KIAA0182 0.65 chr7 54826636-54826706 SEC61G 0.65 chr9 108418404-108418453 MAX.chr9.108418404-108418453 0.65 chr7 64408106-64408135 MAX.chr7.64408106-64408135 0.65 chr10 21816267-21816490 C10orf140 0.65 chr7 39989959-39990020 CDK13 0.65 chr1 240255240-240255264 FMN2 0.65 chr13 114018369-114018421 GRTP1 0.65 chr13 88323571-88323647 LOC642345 0.65 chr5 80256215-80256313 RASGRF2 0.65 chr10 112064230-112064280 SMNDC1 0.65 chr12 85430135-85430175 LRRIQ1 0.65 chr1 241520322-241520334 RGS7 0.65 chr19 22034747-22034887 MAX.chr19.22034747-22034887 0.65 chr21 27011846-27011964 JAM2 0.65 chr11 64052053-64052132 BAD 0.65 chr1 42846119-42846174 RIMKLA 0.64 chr10 17271896-17271994 VIM 0.64 chr13 52378159-52378202 DHRS12 0.63 chr3 27763909-27763981 EOMES 0.63 chr7 100136884-100137350 AGFG2 0.62 chr6 88876701-88876726 CNR1 0.62 chr19 2290471-2290541 LINGO3 0.62 chr6 105584524-105584800 BVES 0.61 chr16 23607524-23607650 NDUFAB1 0.61 chr11 64008415-64008495 FKBP2 0.60 chr20 3641457-3641537 GFRA4 0.59 chr19 4343896-4242968 MPND 0.59 chr2 107503155-107503391 ST6GAL2 0.59 chr1 240161479-240161546 MAX.chr1.240161479-240161546 0.57 chr6 144384503-144385539 PLAGL1 0.57 chr3 72496092-72496361 RYBP 0.57 chr5 131132146-131132232 FNIP1 0.55 chr17 36762706-36762763 SRCIN1 0.55 chr11 32460759-32460800 WT1 0.55 chr9 127266951-127267032 NR5A1 0.53 chr7 44084171-44084235 DBNL 0.46 chr15 29131299-29131369 APBA2 0.44 chr5 114880375-114880442 FEM1C 0.44 chr19 34287890-34287972 KCTD15 0.44 chr16 77468655-77468742 ADAMTS18 chr22 45898798-45898888 FBLN1 chr7 113727624-113727693 FOXP2 chr7 43152309-43152375 HECW1 chr20 20345123-20345150 INSM1 chr20 61637950-61638000 LOC63930 chr1 156406057-156406118 MAX.chr1.156406057-156406118 chr10 23480864-23480913 PTF1A chr5 1445384-1445473 SLC6A3 chr2 107502978-107503055 ST6GAL2 chr10 17496177-17496310 ST8SIA6

All publications and patents mentioned in the above specification are herein incorporated by reference in their entirety for all purposes. Various modifications and variations of the described compositions, methods, and uses of the technology will be apparent to those skilled in the art without departing from the scope and spirit of the technology as described. Although the technology has been described in connection with specific exemplary embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the described modes for carrying out the invention that are obvious to those skilled in pharmacology, biochemistry, medical science, or related fields are intended to be within the scope of the following claims. 

We claim:
 1. A method of screening for a neoplasm in a sample obtained from a subject, the method comprising: 1) assaying a methylation state of a marker in a sample obtained from a subject; and 2) identifying the subject as having a neoplasm when the methylation state of the marker is different than a methylation state of the marker assayed in a subject that does not have a neoplasm, wherein the marker comprises a base in a differentially methylated region (DMR) selected from a group consisting of DMR 1-107 from Table 1, DMR 1-449 from Table 10, and a DMR corresponding to Chr16:58497395-58497458. 